Applying Machine Learning to Tackle the Double Burden of HIV and Diabetes in Rwanda

Applying Machine Learning to Tackle the Double Burden of HIV and Diabetes in Rwanda | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Applying Machine Learning to Tackle the Double Burden of HIV and Diabetes in Rwanda Isaac Komezusenge, Eric Remera, Melissa Uwase, Emmanuel Christian Nyabyenda, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5768967/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Background: HIV and Type 2 diabetes (T2DM) are global health challenges, and the burden of HIV is pronounced in sub Saharan Africa, with the rising prevalence of non-communicable diseases (NCDs) such as T2DM. Objective: The study was to apply machine learning techniques to explore: (i) the proportion of T2DM among people living with HIV (PLWH); and (ii) the association between HIV and diabetes. Methods: The analysis utilized a dataset of 774,189 electronic medical records (EMR) obtained from 10 healthcare facilities in Rwanda between 2019 and 2023. Machine learning models, including logistic regression, random forests, and gradient boosting machines (GBM), were applied to predict the onset of diabetes and evaluate the impact of coexistence of HIV and Diabetes. Statistical analysis was conducted to assess the performance of these models based on accuracy, precision, recall, and F1-score, alongside identifying key risk factors like age, BMI, and blood sugar levels. Results: The prevalence of T2DM in the general population was 4.71%, while among PLWH, the prevalence was significantly higher at 10.22%. Logistic regression and random forest models indicated key predictors of diabetes among HIV-positive individuals as BMI, age, and blood sugar levels. Conclusion and implication: This study underscores the complex interplay between HIV and diabetes in Rwanda. Machine learning models demonstrated high accuracy in predicting outcomes, offering valuable insights for clinical management. Integrated care models that focus on managing Diabetes Mellitus in PLWH to mitigate the risks of complications are important. HIV Diabetes Machine Learning Non-Communicable Diseases Antiretroviral Therapy Electronic Medical Records Rwanda Figures Figure 1 Figure 2 Background The co-existence of non-communicable diseases (NCDs) and infectious diseases poses a significant global health challenge, particularly in low- and middle-income countries where healthcare resources are often limited (Osakunor et al., 2018 ). Non-communicable diseases, such as cardiovascular diseases, cancers, chronic respiratory diseases, and diabetes, are responsible for approximately 41 million deaths each year, representing 74% of global mortality (WHO, 2023). The burden is especially heavy in low- and middle-income countries, which account for 77% of these deaths (WHO, 2023). Type 2 Diabetes Mellitus (T2DM) is one of the most prevalent metabolic disorders globally, primarily caused by defective insulin secretion by pancreatic β-cells and insulin resistance in insulin-sensitive tissues (Galicia- Garcia et al., 2020). Concurrently, the HIV epidemic, particularly in sub-Saharan Africa, continues to be a major public health issue, compromising immune function and increasing susceptibility to other health conditions, including T2DM (Moyo et al., 2023 ; White et al., n.d.). The intersection of HIV and T2DM has led to a concerning rise in comorbidities, with people living with HIV (PLWH) at a higher risk of developing T2DM than the general population (Mesfin Belay et al., 2021 ; Peer et al., 2023 ). The interaction between HIV and T2DM is particularly concerning. Both HIV and T2DM are significant risk factors for different conditions, and their co-occurrence exacerbates these risks (Saran et al., 2020). In Rwanda, where the prevalence of HIV is estimated at 3.0% among adults aged 15–64 years (Nsanzimana et al., 2022 ), and the prevalence of diabetes is 5.3% among adults aged 18–69 years (RBC, 2022 ), the dual burden of these diseases poses a critical public health challenge. Objectives This study aims to apply machine learning techniques to explore: The proportion of T2DM among people living with HIV (PLWH) To investigate the association between HIV and Diabetes T2DM poses a growing concern worldwide, with increasing prevalence among PLWH (Khan et al., 2020 ; Peer et al., 2023 ). This research aims to address specific gaps in understanding the intersection of HIV and T2DM. We strive to provide targeted insights into better T2DM management and minimize the bad outcomes in the Rwandan population. This study's impacts lie in informing healthcare practices, leading to more effective interventions tailored to the specific needs of individuals coexisting with HIV and T2DM in Rwanda. Additionally, the study's significance extends to the integration of an innovative approach to building a machine learning (ML) model (Sarker, 2021 ). By incorporating advanced technology, the research aims to enhance the accuracy and efficiency of predicting diabetes risk among people living with HIV (PLHIV). Methods Study Design and Data Collection A retrospective observational study used a large dataset of electronic medical records (EMR) collected from 10 healthcare facilities in Rwanda. The dataset includes 774,189 patient records gathered between 2019 and 2023, representing a wide demographic range. Each record contained information on HIV status, diabetes diagnosis, and clinical indicators like blood sugar, BMI, and age. Data on ART regimens were not explicitly analyzed, but previous studies suggest ART plays a role in the development of diabetes (Capeau et al., 2012 ; Hernandez-Romieu et al., 2009 ). The dataset was pre-processed to handle missing data and standardize categorical variables. Age was categorized into meaningful groups (e.g., 15–24, 25–34, 35–49, 50–64, 65+), and BMI was categorized into underweight, normal weight, overweight, and obese. Machine Learning Techniques Several machine learning models were applied to assess the risk of T2DM among PLWH: Logistic Regression : Applied to assess the strength of associations between HIV status, and diabetes. Odds ratios (ORs) were calculated to quantify the likelihood of diabetes in PLWH compared to HIV-negative individuals. Random Forests : Selected for its ability to handle large datasets and feature selection. Gradient Boosting Machines (GBM) : Used to enhance prediction accuracy and identify the additional risks due to co-existance of HIV and diabetes. Cross-validation techniques were used to prevent overfitting, and grid search was employed to optimize hyperparameters. Model performance was assessed based on accuracy, precision, recall, F1-score, and area under the curve (AUC) for ROC analysis. Statistical Analysis Descriptive statistics were computed to summarize the demographic and clinical characteristics of the cohort. Chi-squared tests were used to examine associations between categorical variables (e.g., HIV and diabetes status), while logistic regression was applied to calculate odds ratios for developing diabetes. Continuous variables such as blood sugar levels were compared using t-tests or non-parametric tests, depending on the data distribution. Results Proportion of Type II Diabetes Among PLWH Prevalence of HIV: 3.44% Prevalence of Type II Diabetes Mellitus: 4.71% These figures provide a baseline understanding of how widespread each condition is within the overall population. Among the subset of patients who are HIV-positive, the results show that 2,722 out of the 26,627 HIV- positive patients were also diagnosed with Type II diabetes. This indicates a proportion of 10.22% of PLWH who have diabetes. When comparing this proportion to the general population, the prevalence of Type II diabetes among PLWH (10.22%) is significantly higher than the overall prevalence of diabetes in the entire dataset (4.71%). Association Between HIV and Diabetes Logistic regression analysis indicated a strong association between HIV-positive status and an increased likelihood of developing diabetes. The odds ratio for diabetes in PLWH was 2.41 (p < 0.001), indicating that PLWH is more than twice as likely to develop diabetes compared to HIV-negative individuals. Significant predictors identified through random forest analysis included age, BMI, and blood sugar levels, with older age and higher BMI contributing to an increased risk of diabetes. These findings align with previous research confirming a higher prevalence of diabetes in HIV-positive populations (Butt et al., 2009 ). Table 1 Demographic factors associated with Diabetes in HIV Variable OR 95% CI Lower 95% CI Upper AOR 95% CI Lower 95% CI Upper Intercept 0.09 0.07 0.12 0.04 0.03 0.04 Age Group 15–24 Reference Reference 25–34 1.08 0.97 1.21 1.16 1.11 1.21 Age Group 35–49 1.24 1.1 1.4 1.69 1.62 1.76 Age Group 50–64 2.03 1.75 2.37 2.79 2.68 2.91 Age Group 65+ 1.79 1.43 2.24 3.28 3.14 3.42 Gender Female Reference Reference Male 0.75 0.66 0.85 1.02 0.99 1.05 BMI Category Underweight Reference Reference Normal Weight 0.91 0.74 1.12 0.7 0.67 0.74 Overweight 1.08 0.87 1.34 1.13 1.07 1.2 Obese 1.11 0.88 1.39 1.43 1.35 1.52 Province East Reference Reference CoK (Kigali) 1.41 1.24 1.61 1.24 1.19 1.3 North 1.09 0.94 1.27 0.8 0.77 0.84 South 0.91 0.77 1.08 1.03 0.99 1.07 West 1.00 0.85 1.17 0.97 0.94 1.01 Unknown 0.46 0.14 1.49 0.95 0.8 1.13 Discussion The findings from this study provide critical insights into the complex relationship between HIV, Type II diabetes in Rwanda. These results have important implications for public health, clinical practice, and future research. The significant prevalence of Type II diabetes among PLWH − 10.22% - underscores the pressing need for integrated care strategies that address both HIV and diabetes. Public health programs must prioritize the screening and management of diabetes within the HIV-positive population to mitigate the compounded health risks associated with these comorbid conditions. Clinically, the identification of BMI, age, and blood sugar levels as key predictors of Diabetes suggests that healthcare providers should adopt a more holistic approach to managing PLWH. Regular monitoring of these parameters, along with targeted interventions such as weight management and glycemic control, could be vital in preventing renal complications. The findings also emphasize the need for age-specific strategies, as older PLWH are at greater risk of renal impairment. The complexity of the relationship between HIV, diabetes, and renal impairment, as revealed by the machine learning models, highlights the need for further research. Future studies should explore additional factors, such as lifestyle, genetic predispositions, and the effects of antiretroviral therapy (ART), to gain a more comprehensive understanding of these relationships. Policymakers should consider developing integrated care protocols that allocate resources to address the dual challenges of HIV and diabetes in resource-limited settings like Rwanda. While this study provides valuable insights, it is important to acknowledge its limitations. The reliance on retrospective EMR data may introduce biases related to data completeness and accuracy. Additionally, the cross-sectional design limits the ability to infer causal relationships between HIV, diabetes, and renal impairment. Longitudinal studies could provide a more robust understanding of these relationships over time. Future research should also investigate the impact of ART regimens on the development of diabetes and renal impairment, as well as explore the role of social determinants of health in these outcomes. Conclusion This research has shed light on the double burden of HIV and diabetes in Rwanda, focusing on the prevalence of Type II diabetes among people living with HIV (PLWH) and the factors contributing to Diabetes in PLWH. The findings reveal that a significant proportion of PLWH − 10.22% - also suffer from Type II diabetes, emphasizing the importance of addressing this comorbidity within the HIV- positive population. The study also underscores the complexity of the relationship between HIV and diabetes, suggesting that additional factors, such as BMI, age, and blood sugar levels, play a critical role in the development of Diabetes. These conclusions highlight the necessity for a holistic and integrated approach to managing PLWH, with a particular emphasis on monitoring and mitigating the risks associated with diabetes. Addressing these factors comprehensively can lead to better health outcomes and improved quality of life for this vulnerable population. Future research should explore the long-term effects of ART on metabolic health, as well as the potential for deep learning models to further improve predictive accuracy in resource-constrained settings. In conclusion, addressing the double burden of HIV and Type II diabetes in Rwanda requires a multifaceted approach that includes enhanced screening, comprehensive management strategies, further research, policy integration, and patient education. By focusing on these areas, healthcare providers can improve outcomes for this vulnerable population and reduce the impact of these chronic conditions on their lives. Declarations Ethics approval and consent to participate Ethical approval for this study was obtained from the College of Medicine and Health Sciences Institutional Review Board (CMHS-IRB) at the University of Rwanda. The research was conducted in compliance with the guidelines and regulations outlined by the board to ensure the ethical handling of human data. The reference number for the ethical clearance is CMHS/IRB/340/2024. This study involved secondary data analysis of anonymized electronic medical records. As such, no direct interaction with participants was required, and obtaining individual informed consent was deemed unnecessary by the CMHS-IRB in accordance with national regulations. All data were anonymized prior to analysis to ensure the privacy and confidentiality of the individuals involved. Consent for publication Not applicable. Availability of data and material The dataset used in this study contains sensitive medical records and cannot be shared publicly due to privacy and confidentiality concerns. Access to the data can be requested from the relevant healthcare institutions in Rwanda, subject to ethical approval. Competing interests The authors declare that they have no competing interests. Funding This research was not supported by any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' contributions Isaac Komezusenge conducted the data analysis, interpreted the results, and drafted the manuscript. Eric Remera and David K. Tumusiime provided guidance, reviewed the manuscript, and contributed to the study design and interpretation of findings. All authors read and approved the final manuscript. Acknowledgments The authors wish to thank the University of Rwanda and the Rwanda Biomedical Center for granting access to the data used in this research. Special thanks to Louise and other friends and colleagues who provided valuable advice and support throughout this study. Authors' information Isaac Komezusenge completed a Master's degree in Data Science specializing in Biostatistics from the African Centre of Excellence in Data Science (ACE-DS), University of Rwanda. His research focuses on applying machine learning and data science techniques to tackle healthcare challenges in Rwanda, particularly in addressing the double burden of infectious and non-communicable diseases. References Afkarian M, Zelnick LR, Hall YN, Heagerty PJ, Tuttle K, Weiss NS, De Boer IH. Clinical manifestations of kidney disease among US adults with diabetes, 1988–2014. JAMA - J Am Med Association. 2016;316(6):602–10. https://doi.org/10.1001/jama.2016.10924 . Benk M, Ferrario A. Explaining Interpretable Machine Learning: Theory, Methods and Applications. SSRN Electron J. 2021. https://doi.org/10.2139/ssrn.3748268 . 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5768967","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":479714473,"identity":"d26722e8-5756-11f0-91e4-06cc9d20a69f","order_by":0,"name":"Isaac Komezusenge","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACNjB5AEwyPgASPHykaGE2AGlhI84uiBY2CYQheAAf/+GHH36csYnm5z/8rPJrjp0MGwPzw0c38DlMIs1YsudGWu7MhmNmt2W3JQMdxmZsnINXC4OBNMOHw7kbDjaY3ZbcxgzUwsMmjVcL//HPv0Fa9h9m/1Ysua2eCC0MOWbSDDeAtrDxmDF+3HaYCC0SOWWWPWfScmec4SmWZtx2nIeNmYBf5PuPb77x45hNbn//8Y0ff26rtudnb374GJ8WFMDMAyaJVQ4CjD9IUT0KRsEoGAUjBgAAQuVGIShfYCgAAAAASUVORK5CYII=","orcid":"","institution":"University of Rwanda","correspondingAuthor":true,"prefix":"","firstName":"Isaac","middleName":"","lastName":"Komezusenge","suffix":""},{"id":479715013,"identity":"fce19e0b-5756-11f0-91e4-06cc9d20a69f","order_by":1,"name":"Eric Remera","email":"","orcid":"","institution":"Rwanda Biomedical Center","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"","lastName":"Remera","suffix":""},{"id":479715035,"identity":"0533a243-5757-11f0-91e4-06cc9d20a69f","order_by":2,"name":"Melissa Uwase","email":"","orcid":"","institution":"University of Rwanda","correspondingAuthor":false,"prefix":"","firstName":"Melissa","middleName":"","lastName":"Uwase","suffix":""},{"id":479715117,"identity":"1185546c-5757-11f0-91e4-06cc9d20a69f","order_by":3,"name":"Emmanuel Christian Nyabyenda","email":"","orcid":"","institution":"University of Rwanda","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"Christian","lastName":"Nyabyenda","suffix":""},{"id":479715175,"identity":"1a70da73-5757-11f0-91e4-06cc9d20a69f","order_by":4,"name":"Sylvain Hirwa Muzungu","email":"","orcid":"","institution":"University of Rwanda","correspondingAuthor":false,"prefix":"","firstName":"Sylvain","middleName":"Hirwa","lastName":"Muzungu","suffix":""},{"id":479715271,"identity":"277d0061-5757-11f0-91e4-06cc9d20a69f","order_by":4,"name":"Sylvain Hirwa Muzungu","email":"","orcid":"","institution":"University of Rwanda","correspondingAuthor":false,"prefix":"","firstName":"Sylvain","middleName":"Hirwa","lastName":"Muzungu","suffix":""},{"id":479715314,"identity":"300e2801-5757-11f0-91e4-06cc9d20a69f","order_by":5,"name":"Fauste Ndikumana","email":"","orcid":"","institution":"University of Rwanda","correspondingAuthor":false,"prefix":"","firstName":"Fauste","middleName":"","lastName":"Ndikumana","suffix":""},{"id":479715382,"identity":"39e5500b-5757-11f0-91e4-06cc9d20a69f","order_by":6,"name":"David K. Tumusiime","email":"","orcid":"","institution":"University of Rwanda","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"K.","lastName":"Tumusiime","suffix":""}],"badges":[],"createdAt":"2025-01-05 17:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5768967/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5768967/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85992082,"identity":"306fdb13-0cf4-4912-bbae-4e24449c60e6","added_by":"auto","created_at":"2025-07-04 05:30:55","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21127,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of Type II Diabetes among PLWH\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5768967/v1/c816039554e8040f081e42ab.jpeg"},{"id":85992075,"identity":"0f5d526b-d54b-49b8-b609-17e69299cfa0","added_by":"auto","created_at":"2025-07-04 05:30:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23483,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of Diabetes in HIV-negative and HIV-positive Patients\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5768967/v1/364794a11da25a9b3aa82514.png"},{"id":85993323,"identity":"afc2dcd4-df64-4be4-9094-5f767b56e527","added_by":"auto","created_at":"2025-07-04 05:38:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":650675,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5768967/v1/b01c67b2-d1b3-468a-b14b-c95ec78c8390.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Applying Machine Learning to Tackle the Double Burden of HIV and Diabetes in Rwanda","fulltext":[{"header":"Background","content":"\u003cp\u003eThe co-existence of non-communicable diseases (NCDs) and infectious diseases poses a significant global health challenge, particularly in low- and middle-income countries where healthcare resources are often limited (Osakunor et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Non-communicable diseases, such as cardiovascular diseases, cancers, chronic respiratory diseases, and diabetes, are responsible for approximately 41\u0026nbsp;million deaths each year, representing 74% of global mortality (WHO, 2023). The burden is especially heavy in low- and middle-income countries, which account for 77% of these deaths (WHO, 2023).\u003c/p\u003e \u003cp\u003eType 2 Diabetes Mellitus (T2DM) is one of the most prevalent metabolic disorders globally, primarily caused by defective insulin secretion by pancreatic β-cells and insulin resistance in insulin-sensitive tissues (Galicia- Garcia et al., 2020). Concurrently, the HIV epidemic, particularly in sub-Saharan Africa, continues to be a major public health issue, compromising immune function and increasing susceptibility to other health conditions, including T2DM (Moyo et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; White et al., n.d.). The intersection of HIV and T2DM has led to a concerning rise in comorbidities, with people living with HIV (PLWH) at a higher risk of developing T2DM than the general population (Mesfin Belay et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Peer et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe interaction between HIV and T2DM is particularly concerning. Both HIV and T2DM are significant risk factors for different conditions, and their co-occurrence exacerbates these risks (Saran et al., 2020).\u003c/p\u003e \u003cp\u003eIn Rwanda, where the prevalence of HIV is estimated at 3.0% among adults aged 15\u0026ndash;64 years (Nsanzimana et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and the prevalence of diabetes is 5.3% among adults aged 18\u0026ndash;69 years (RBC, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the dual burden of these diseases poses a critical public health challenge.\u003c/p\u003e \u003cp\u003eObjectives\u003c/p\u003e \u003cp\u003eThis study aims to apply machine learning techniques to explore:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe proportion of T2DM among people living with HIV (PLWH)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo investigate the association between HIV and Diabetes\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eT2DM poses a growing concern worldwide, with increasing prevalence among PLWH (Khan et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Peer et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This research aims to address specific gaps in understanding the intersection of HIV and T2DM.\u003c/p\u003e \u003cp\u003eWe strive to provide targeted insights into better T2DM management and minimize the bad outcomes in the Rwandan population. This study's impacts lie in informing healthcare practices, leading to more effective interventions tailored to the specific needs of individuals coexisting with HIV and T2DM in Rwanda.\u003c/p\u003e \u003cp\u003eAdditionally, the study's significance extends to the integration of an innovative approach to building a machine learning (ML) model (Sarker, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By incorporating advanced technology, the research aims to enhance the accuracy and efficiency of predicting diabetes risk among people living with HIV (PLHIV).\u003c/p\u003e "},{"header":"Methods","content":" \u003cp\u003eStudy Design and Data Collection\u003c/p\u003e \u003cp\u003eA retrospective observational study used a large dataset of electronic medical records (EMR) collected from 10 healthcare facilities in Rwanda. The dataset includes 774,189 patient records gathered between 2019 and 2023, representing a wide demographic range. Each record contained information on HIV status, diabetes diagnosis, and clinical indicators like blood sugar, BMI, and age. Data on ART regimens were not explicitly analyzed, but previous studies suggest ART plays a role in the development of diabetes (Capeau et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hernandez-Romieu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe dataset was pre-processed to handle missing data and standardize categorical variables. Age was categorized into meaningful groups (e.g., 15\u0026ndash;24, 25\u0026ndash;34, 35\u0026ndash;49, 50\u0026ndash;64, 65+), and BMI was categorized into underweight, normal weight, overweight, and obese.\u003c/p\u003e \u003cp\u003eMachine Learning Techniques\u003c/p\u003e \u003cp\u003eSeveral machine learning models were applied to assess the risk of T2DM among PLWH:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLogistic Regression\u003c/b\u003e: Applied to assess the strength of associations between HIV status, and diabetes. Odds ratios (ORs) were calculated to quantify the likelihood of diabetes in PLWH compared to HIV-negative individuals.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRandom Forests\u003c/b\u003e: Selected for its ability to handle large datasets and feature selection.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGradient Boosting Machines (GBM)\u003c/b\u003e: Used to enhance prediction accuracy and identify the additional risks due to co-existance of HIV and diabetes.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eCross-validation techniques were used to prevent overfitting, and grid search was employed to optimize hyperparameters. Model performance was assessed based on accuracy, precision, recall, F1-score, and area under the curve (AUC) for ROC analysis.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were computed to summarize the demographic and clinical characteristics of the cohort. Chi-squared tests were used to examine associations between categorical variables (e.g., HIV and diabetes status), while logistic regression was applied to calculate odds ratios for developing diabetes. Continuous variables such as blood sugar levels were compared using t-tests or non-parametric tests, depending on the data distribution.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eProportion of Type II Diabetes Among PLWH\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePrevalence of HIV: 3.44%\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePrevalence of Type II Diabetes Mellitus: 4.71%\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese figures provide a baseline understanding of how widespread each condition is within the overall population.\u003c/p\u003e \u003cp\u003eAmong the subset of patients who are HIV-positive, the results show that 2,722 out of the 26,627 HIV- positive patients were also diagnosed with Type II diabetes. This indicates a \u003cb\u003eproportion of 10.22%\u003c/b\u003e of PLWH who have diabetes.\u003c/p\u003e \u003cp\u003eWhen comparing this proportion to the general population, the prevalence of Type II diabetes among PLWH (10.22%) is significantly higher than the overall prevalence of diabetes in the entire dataset (4.71%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAssociation Between HIV and Diabetes\u003c/p\u003e \u003cp\u003eLogistic regression analysis indicated a strong association between HIV-positive status and an increased likelihood of developing diabetes. The odds ratio for diabetes in PLWH was 2.41 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that PLWH is more than twice as likely to develop diabetes compared to HIV-negative individuals. Significant predictors identified through random forest analysis included age, BMI, and blood sugar levels, with older age and higher BMI contributing to an increased risk of diabetes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese findings align with previous research confirming a higher prevalence of diabetes in HIV-positive populations (Butt et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic factors associated with Diabetes in HIV\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI Lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI Upper\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI Lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI Upper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntercept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Group 35\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Group 50\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Group 65+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI Category\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal Weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProvince\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoK (Kigali)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe findings from this study provide critical insights into the complex relationship between HIV, Type II diabetes in Rwanda. These results have important implications for public health, clinical practice, and future research.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe significant prevalence of Type II diabetes among PLWH \u0026minus;\u0026thinsp;10.22% - underscores the pressing need for integrated care strategies that address both HIV and diabetes. Public health programs must prioritize the screening and management of diabetes within the HIV-positive population to mitigate the compounded health risks associated with these comorbid conditions.\u003c/p\u003e \u003cp\u003eClinically, the identification of BMI, age, and blood sugar levels as key predictors of Diabetes suggests that healthcare providers should adopt a more holistic approach to managing PLWH. Regular monitoring of these parameters, along with targeted interventions such as weight management and glycemic control, could be vital in preventing renal complications. The findings also emphasize the need for age-specific strategies, as older PLWH are at greater risk of renal impairment.\u003c/p\u003e \u003cp\u003eThe complexity of the relationship between HIV, diabetes, and renal impairment, as revealed by the machine learning models, highlights the need for further research. Future studies should explore additional factors, such as lifestyle, genetic predispositions, and the effects of antiretroviral therapy (ART), to gain a more comprehensive understanding of these relationships. Policymakers should consider developing integrated care protocols that allocate resources to address the dual challenges of HIV and diabetes in resource-limited settings like Rwanda.\u003c/p\u003e \u003cp\u003eWhile this study provides valuable insights, it is important to acknowledge its limitations. The reliance on retrospective EMR data may introduce biases related to data completeness and accuracy. Additionally, the cross-sectional design limits the ability to infer causal relationships between HIV, diabetes, and renal impairment. Longitudinal studies could provide a more robust understanding of these relationships over time. Future research should also investigate the impact of ART regimens on the development of diabetes and renal impairment, as well as explore the role of social determinants of health in these outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research has shed light on the double burden of HIV and diabetes in Rwanda, focusing on the prevalence of Type II diabetes among people living with HIV (PLWH) and the factors contributing to Diabetes in PLWH. The findings reveal that a significant proportion of PLWH \u0026minus;\u0026thinsp;10.22% - also suffer from Type II diabetes, emphasizing the importance of addressing this comorbidity within the HIV- positive population. The study also underscores the complexity of the relationship between HIV and diabetes, suggesting that additional factors, such as BMI, age, and blood sugar levels, play a critical role in the development of Diabetes.\u003c/p\u003e \u003cp\u003eThese conclusions highlight the necessity for a holistic and integrated approach to managing PLWH, with a particular emphasis on monitoring and mitigating the risks associated with diabetes. Addressing these factors comprehensively can lead to better health outcomes and improved quality of life for this vulnerable population. Future research should explore the long-term effects of ART on metabolic health, as well as the potential for deep learning models to further improve predictive accuracy in resource-constrained settings.\u003c/p\u003e \u003cp\u003eIn conclusion, addressing the double burden of HIV and Type II diabetes in Rwanda requires a multifaceted approach that includes enhanced screening, comprehensive management strategies, further research, policy integration, and patient education. By focusing on these areas, healthcare providers can improve outcomes for this vulnerable population and reduce the impact of these chronic conditions on their lives.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eEthical approval for this study was obtained from the College of Medicine and Health Sciences Institutional Review Board (CMHS-IRB) at the University of Rwanda. The research was conducted in compliance with the guidelines and regulations outlined by the board to ensure the ethical handling of human data. The reference number for the ethical clearance is CMHS/IRB/340/2024.\u003c/p\u003e\n\u003cp\u003eThis study involved secondary data analysis of anonymized electronic medical records. As such, no direct interaction with participants was required, and obtaining individual informed consent was deemed unnecessary by the CMHS-IRB in accordance with national regulations. All data were anonymized prior to analysis to ensure the privacy and confidentiality of the individuals involved.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and material\u003c/h2\u003e\n\u003cp\u003eThe dataset used in this study contains sensitive medical records and cannot be shared publicly due to privacy and confidentiality concerns. Access to the data can be requested from the relevant healthcare institutions in Rwanda, subject to ethical approval.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research was not supported by any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eIsaac Komezusenge conducted the data analysis, interpreted the results, and drafted the manuscript. Eric Remera and David K. Tumusiime provided guidance, reviewed the manuscript, and contributed to the study design and interpretation of findings. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThe authors wish to thank the University of Rwanda and the Rwanda Biomedical Center for granting access to the data used in this research. Special thanks to Louise and other friends and colleagues who provided valuable advice and support throughout this study.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; information\u003c/h2\u003e\n\u003cp\u003eIsaac Komezusenge completed a Master\u0026apos;s degree in Data Science specializing in Biostatistics from the African Centre of Excellence in Data Science (ACE-DS), University of Rwanda. His research focuses on applying machine learning and data science techniques to tackle healthcare challenges in Rwanda, particularly in addressing the double burden of infectious and non-communicable diseases.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAfkarian M, Zelnick LR, Hall YN, Heagerty PJ, Tuttle K, Weiss NS, De Boer IH. Clinical manifestations of kidney disease among US adults with diabetes, 1988\u0026ndash;2014. JAMA - J Am Med Association. 2016;316(6):602\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.2016.10924\u003c/span\u003e\u003cspan address=\"10.1001/jama.2016.10924\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenk M, Ferrario A. Explaining Interpretable Machine Learning: Theory, Methods and Applications. 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(n.d.). \u003cem\u003eThe Effects of HIV on the Body: Immune System and More\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.healthline.com/health/hiv-aids/effects-on-body#takeaway\u003c/span\u003e\u003cspan address=\"https://www.healthline.com/health/hiv-aids/effects-on-body#takeaway\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cem\u003eThe Effects of HIV on the Body Medically reviewed\u003c/em\u003e. https://www.healthline.com/health/hiv-aids/effects-on-body#takeaway\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. (2023). Noncommunicable diseases. WHO. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact- sheets/detail/noncommunicable-diseases\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact- sheets/detail/noncommunicable-diseases\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HIV, Diabetes, Machine Learning, Non-Communicable Diseases, Antiretroviral Therapy, Electronic Medical Records, Rwanda","lastPublishedDoi":"10.21203/rs.3.rs-5768967/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5768967/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground:\u003c/p\u003e\n\u003cp\u003eHIV and Type 2 diabetes (T2DM) are global health challenges, and the burden of HIV is pronounced in sub Saharan Africa, with the rising prevalence of non-communicable diseases (NCDs) such as T2DM.\u003c/p\u003e\n\u003cp\u003eObjective:\u003c/p\u003e\n\u003cp\u003eThe study was to apply machine learning techniques to explore: (i) the proportion of T2DM among people living with HIV (PLWH); and (ii) the association between HIV and diabetes.\u003c/p\u003e\n\u003cp\u003eMethods:\u003c/p\u003e\n\u003cp\u003eThe analysis utilized a dataset of 774,189 electronic medical records (EMR) obtained from 10 healthcare facilities in Rwanda between 2019 and 2023. Machine learning models, including logistic regression, random forests, and gradient boosting machines (GBM), were applied to predict the onset of diabetes and evaluate the impact of coexistence of HIV and Diabetes. Statistical analysis was conducted to assess the performance of these models based on accuracy, precision, recall, and F1-score, alongside identifying key risk factors like age, BMI, and blood sugar levels.\u003c/p\u003e\n\u003cp\u003eResults:\u003c/p\u003e\n\u003cp\u003eThe prevalence of T2DM in the general population was 4.71%, while among PLWH, the prevalence was significantly higher at 10.22%. Logistic regression and random forest models indicated key predictors of diabetes among HIV-positive individuals as BMI, age, and blood sugar levels.\u003c/p\u003e\n\u003cp\u003eConclusion and implication:\u003c/p\u003e\n\u003cp\u003eThis study underscores the complex interplay between HIV and diabetes in Rwanda. Machine learning models demonstrated high accuracy in predicting outcomes, offering valuable insights for clinical management. Integrated care models that focus on managing Diabetes Mellitus in PLWH to mitigate the risks of complications are important.\u003c/p\u003e","manuscriptTitle":"Applying Machine Learning to Tackle the Double Burden of HIV and Diabetes in Rwanda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-04 05:30:50","doi":"10.21203/rs.3.rs-5768967/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-26T04:07:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189598288972308489441920333837738186899","date":"2026-04-16T04:27:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257325793914767381536685124036019404309","date":"2026-04-15T07:54:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T02:43:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227142966387750331258975556162439170367","date":"2026-04-13T02:30:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T10:48:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246451172457002850795898854505114502061","date":"2026-04-10T07:34:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74546382770370104773070374016789750899","date":"2025-03-14T08:40:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-09T12:50:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329783947157551711655835814731298150606","date":"2025-03-07T14:23:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31479397089427313260263320751329170977","date":"2025-03-06T11:43:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-01T09:14:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-24T15:30:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-01-22T13:54:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-17T08:32:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Informatics and Decision Making","date":"2025-01-17T08:31:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3ca72575-7c93-478d-9bf4-ba1081442873","owner":[],"postedDate":"July 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-04T05:30:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-04 05:30:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5768967","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5768967","identity":"rs-5768967","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}