Harnessing Machine Learning for Antimicrobial Resistance Surveillance in Zimbabwe

Harnessing Machine Learning for Antimicrobial Resistance Surveillance in Zimbabwe | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Harnessing Machine Learning for Antimicrobial Resistance Surveillance in Zimbabwe View ORCID Profile Liberty Mutahwa , Hilary Takunda Takawira , Tinashe Muteveri , View ORCID Profile Perkins Watambwa , Delson Chikobvu , Whatmore Sengweni , Claris Siyamayambo , Justice Kasiroori , Lyson Chaka , Farai Mlambo doi: https://doi.org/10.1101/2025.09.02.25334969 Liberty Mutahwa 1 Midlands State University, Department of Applied Biosciences and Biotechnology , Gweru, Zimbabwe Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Liberty Mutahwa For correspondence: mutahwaliberty{at}gmail.com Hilary Takunda Takawira 1 Midlands State University, Department of Applied Biosciences and Biotechnology , Gweru, Zimbabwe Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tinashe Muteveri 1 Midlands State University, Department of Applied Biosciences and Biotechnology , Gweru, Zimbabwe Find this author on Google Scholar Find this author on PubMed Search for this author on this site Perkins Watambwa 2 University of Zimbabwe, Department of Mathematics and Computational Sciences , Harare, Zimbabwe Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Perkins Watambwa Delson Chikobvu 3 University of the Free State , Bloemfontein, South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site Whatmore Sengweni 4 Midlands State University, Department of Applied Mathematics and Statistics , Gweru, Zimbabwe Find this author on Google Scholar Find this author on PubMed Search for this author on this site Claris Siyamayambo 5 South Africa Medical Research Council/ University of Johannesburg (SAMRC/UJ) Pan African Centre for Epidemics Research (PACER) Extramural Unit, Faculty of Health Sciences , South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site Justice Kasiroori 6 University of Zimbabwe, Department of Analytics and Informatics , Harare, Zimbabwe Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lyson Chaka 7 University of Pretoria, Department of Statistics , Pretoria, South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site Farai Mlambo 8 University of the Witwatersrand, Graduate School of Business Administration , Johannesburg, South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Data/Code Preview PDF ABSTRACT Antimicrobial resistance (AMR) poses a significant public health challenge, particularly in resource-limited settings such as Zimbabwe, where surveillance systems are often underdeveloped. This study aims to characterise AMR patterns at the Gweru provincial hospital (GPH) and evaluate machine learning (ML) models for predicting resistance to enhance surveillance. This retrospective cross-sectional study comprised 4 054 clinical isolates from 874 patient records (2022–2024). Five ML models, namely, support vector machine (SVM), random forest, logistic regression, gradient boosting, and k-nearest neighbors (KNN), were trained and evaluated, focusing on predictive performance for surveillance purposes. Among all evaluated models, SVM achieved the highest accuracy (72.08%), precision (73.25%), recall (79.78%), F1 score (0.76), and AUC-ROC (0.79), indicating it as the most effective model for AMR surveillance in this study. Feature importance analysis revealed that antibiotic class, hospital ward, patient age, and pathogen type were significant predictors of resistance. Notably, resistance was high for tetracycline (72.1%) and nitrofurantoin (75.7%), whereas imipenem (7.7%) showed the lowest resistance rates. Multidrug resistance was high among S. aureus (30%), whereas Shigella spp. and Serratia marcescens showed no multidrug resistance. This study highlights the significant AMR burden in Gweru and demonstrates the potential of ML, particularly SVM, for use in predictive surveillance. These findings support targeted interventions in high-risk hospital wards against specific pathogens, offering a scalable approach to AMR monitoring in resource-limited settings. 1. Introduction Antimicrobial resistance has become one of the most significant global health issues of the 21st century, compromising the efficacy of treatments for infections that would otherwise be treatable (Bo et al., 2024). Extensive overprescription and abuse of antibiotics in clinical practice have hastened the emergence of resistant bacterial populations. In 2019, AMR was directly responsible for an estimated 1.27 million fatalities globally ( Aslam et al ., 2024 ). If current trends continue, it is estimated that antimicrobial resistance can lead to as many as 10 million deaths each year by 2050, with Africa likely to face the highest burden, up to 4.5 million deaths each year ( Sartorius et al ., 2024 ). The projected economic burden of antimicrobial resistance is also significant, with an estimated worldwide cost of US$100 trillion ( Murray et al ., 2022 ). The burden will disproportionately be experienced in sub-Saharan Africa, where healthcare systems are already under pressure and infrastructure is lacking ( Sartorius et al ., 2024 ). Across Africa, the AMR crisis has been driven by a mix of socioeconomic and systemic drivers. Pervasive poverty impacting adherence to expensive medications, frail health systems, and sometimes uncontrolled antibiotic dispensing has fostered conditions conducive to the rapid spread of resistance ( Kariuki et al ., 2022 ; Gulumbe et al ., 2022 ). Africa has the world’s highest AMR-attributed death rate, 27.3 per 100 000 people, surpassing the combined fatalities of HIV/AIDS, tuberculosis, and malaria ( Africa CDC, 2023 ). Despite this catastrophic burden, fewer than one-quarter of sub-Saharan African countries have rolling national action plans to combat AMR ( Aruhomukama and Nakabuye, 2023 ). Zimbabwe is a typical example of these regional challenges. Mhondoro et al . (2019) reviewed more than 23 000 clinical bacterial isolates, identified E. coli (43.2%) and S. aureus (15.8%) as the most prevalent pathogens, and antibiotic resistance to the most widely used drugs like ampicillin and penicillin was in excess of 70%. In Zimbabwe, a total of 3 900 deaths reported in 2021 were directly caused by AMR, and 15,800 deaths were attributed to resistant infections, which signified an increasing public health and economic burden (IHME, 2023). While in Africa, a few nations have made progress in digitised AMR surveillance, such as South Africa’s GERMS-SA, Kenya’s Fleming fund initiative (NICD, 2023; Chetty et al ., 2022 ; Okolie et al ., 2025 ). Zimbabwe has adopted a multisectoral One Health approach to promote collaboration in the human health, agriculture, and environmental sectors ( Ren et al ., 2022 ; Shapiro et al ., 2019 ). However, its implementation has been slow and hindered by the lack of real-time digital surveillance systems ( Pearcy et al ., 2021 ). Zimbabwe’s AMR surveillance remains largely reactive, relying on traditional statistics, which primarily focus on severe cases like multidrug-resistant K. pneumoniae in intensive care units. These methods lack predictive capacity, hindering timely interventions ( Kim et al ., 2022 ). Such limitations are particularly evident in a medium-sized city such as Gweru, where health facilities often operate with minimal staff and inadequate resources for monitoring resistance patterns. The need for increased surveillance is indicated by Mhondoro et al . (2019) , who reported elevated resistance levels in S. aureus , E. coli to commonly used antibiotics including amoxicillin. These results point to the necessity of introducing more comprehensive and prescriptive AMR surveillance systems that can guide empirical treatment guidelines and antimicrobial stewardship programs. Conventional analysis methods, such as linear regression, are not robust enough to handle the complex and high-dimensional data produced by microbiology laboratories ( Chiwodza et al ., 2023 ). Emerging advances in machine learning offer a transformative approach to AMR surveillance, especially in settings with abundant data but limited resources. Machine learning algorithms have the potential to sift through large and diverse datasets, uncovering complex patterns, correlations, and trends that might be missed by traditional statistical methods ( Broschat et al ., 2024 ; Kim et al ., 2022 ; Ren et al ., 2022 ; Sakagianni et al ., 2023 ). Machine learning has demonstrated its predictive capacity, such as predicting resistance profiles in E. coli based on genomic data ( Pearcy et al ., 2021 ). For instance, a multicountry study involving Uganda, Nigeria, and Tanzania used whole genome sequencing (WGS) data to validate ML models, achieving up to 92% accuracy in predicting E. coli resistance to ampicillin ( Nsubuga et al ., 2024 ). In Nigeria, random forest and LightGBM models were applied to predict resistance gene profiles with 81% accuracy ( Adedeji et al ., 2025 ). Babirye et al . (2024) demonstrated ML-based prediction of tuberculosis drug resistance using WGS data, achieving 85% accuracy in forecasting rifampicin resistance in Uganda. However, in Zimbabwe, the use of ML in public health surveillance is still in its early stages. Existing models often rely on global datasets that do not adequately account for local epidemiological, clinical, and environmental factors influencing AMR ( Sunuwar and Azad, 2021 ). This study aimed to explore the potential of ML in improving AMR surveillance and prediction by generating actionable insights from clinical data in a resource-limited country like Zimbabwe. Machine learning models can support infection control measures in Zimbabwe by detecting resistance trends and forecasting future trends, thereby providing evidence-based support for decisions and actions that enhance patient outcomes. The objectives of the study include (i) to develop a predictive ML model for AMR surveillance using clinical data collected from Gweru Provincial Hospital, (ii) to evaluate and select the best performing ML models, and (iii) to identify key pathogens, resistance drivers, and temporal trends that can guide focused interventions suited to the healthcare environment in Zimbabwe. This study sought to equip healthcare professionals with the knowledge and skills necessary to make informed decisions and allocate resources effectively to fight against AMR. 2. Materials and Methods 2.1 Study design This study applied the retrospective cross-sectional design by analysing historically antimicrobial susceptibility tests (AST) results from bacterial cultures of 874 patients at Gweru Provincial Hospital (GPH) collected from 2022 to 2024. 2.2 Variables under consideration The main outcome of this research was the AST result of bacterial isolates from clinical specimens. The results were used to classify bacterial isolates into susceptible, intermediate, or resistant categories to particular antibiotics according to standardised interpretive criteria, such as those of the Clinical and Laboratory Standards Institute (CLSI). The AST reports provided essential information to assess the trend and burden of AMR among frequently isolated pathogens. Multidrug resistance (MDR) in this study was defined as resistance to at least one agent in three or more antimicrobial categories ( Sartorius et al ., 2024 ; Kasew et al ., 2021 ). Resistance was coded as a binary representing resistant (1) vs susceptible (0). This outcome served as a key measure for mapping the prevalence of drug resistance, informing empirical treatment guidelines, and examining temporal trends in resistance proportions across different seasons of the year. 2.3 Inclusion and exclusion criteria All patients with laboratory confirmed bacterial infections were included, regardless of age, sex, or clinical condition, with no demographic restrictions applied. Patients negative for bacterial infections were excluded to focus on AMR patterns. The study analysis followed a multiphase approach ( Figure 1 ): data collection, preprocessing, model development, and evaluation ( Asadi et al ., 2020 ). Download figure Open in new tab Figure 1: Flowchart illustrating the comprehensive approach for antimicrobial resistance prediction, detailing data processing, model development, and evaluation metrics 2.4 Study setting The study was conducted at Gweru Provincial Hospital, a general referral hospital in the Midlands Province, Zimbabwe. The GPH serves as a central hub for healthcare delivery, managing a high volume of infectious disease cases, including those requiring AST. The hospital’s diagnostic laboratory performs routine culture and susceptibility testing but faces challenges such as limited resources and a shortage of electronic data systems, which compromise AMR surveillance efforts. These conditions necessitated the development of a predictive model to enhance local monitoring capacity ( Iskandar et al ., 2021 ). Gweru Provincial Hospital serves both urban and rural populations, offering outpatient and inpatient services, as well as diagnostic microbiology. The hospital’s microbiology laboratory routinely processes specimens from various departments, including maternal, ICU, pediatric, and outpatient departments. The laboratory is equipped to perform standard culture and sensitivity testing, and it also receives referrals from various clinics across the Midlands province. Thus, GPH plays a critical role in regional surveillance of infectious diseases and antimicrobial resistance. These settings provided a valuable opportunity to examine real-world trends in bacterial infections and resistance patterns using routinely collected laboratory data. 2.5 Data source All microbiological tests were conducted by the GPH laboratory as part of its diagnostic services; however, specimen collection, culture, identification, and antimicrobial testing were not performed in this study. Thus, this study was a secondary analysis of routine microbiological data generated at the GPH laboratory. Data were collected from laboratory worksheets with the results of AST for 874 patients. The datasets also contained demographic information and limited clinical histories ( Table 1 ). Data was manually entered into a computerised database using LibreOffice Calc, and double-entry verification was performed to minimize transcription errors. Intermediate results (3%) were recorded as resistant (R) to ensure a binary outcome for model training, as this approach enhances model performance and helps to overcome class imbalance ( Feretzakis et al ., 2020 ). View this table: View inline View popup Download powerpoint Table 1: Overview of the independent variables in patient-related antimicrobial susceptibility tests 2.6 Data preprocessing The initial dataset included 4 126 AST records ( Figure 2 ). After excluding duplicate drug entries within individual sensitivity tests and removing rows with undefined drug codes, 4 054 isolates were retained for ML modeling and surveillance analysis. Download figure Open in new tab Figure 2: Flowchart illustrating the data cleaning process for antimicrobial sensitivity test records Download figure Open in new tab Figure 3: Percentage resistance of bacterial isolates to commonly tested antibiotics Download figure Open in new tab Figure 4: Confusion matrices of the performance of machine learning models Data preprocessing involved several steps to ensure compatibility with ML algorithms. The data format displayed variations, including inconsistent date formats, mixed string cases in categorical variables (for example, “Male” vs. “male”), and inconsistent naming of antibiotics and pathogens (abbreviations vs. full names). Continuous variables, including age, were categorised into age groups to capture non-linear relationships with resistance, which helped the model treat each age group as an independent entity. All categorical variables were transformed using one-hot encoding to convert non-numeric data into a format that models can process without incorrectly assuming an ordinal relationship between categories ( Ali et al ., 2023 ). The final dataset of 4 054 isolates was split into training (3 243 isolates; 80%) and testing (811 isolates; 20%) subsets. The split was performed using the train-test-split function from Scikit-learn (version 1.2.2) with a random seed (42) to ensure reproducibility. 2.7 Model building Algorithm selection was guided by the nature of the dataset, interpretability of the models, and their capacity to perform AMR prediction tasks ( Camacho et al ., 2018 ). Logistic regression, SVM, KNN, and random forests were primarily considered because of their established effectiveness in classification ( Khalid et al ., 2023 ). Gradient boosting was also explored because it is known to enhance predictive accuracy and robustness by aggregating the predictions of multiple base models (Ganie et al ., 2024). Five ML models (logistic regression, random forest, KNN, SVM and gradient boosting) were developed to predict AMR patterns from a cleaned dataset of 3 243 clinical isolate records from 874 patient records at GPH. All models were implemented in Python 3.10 using Scikit-learn (version 1.2.2) and pandas. A grid search combined with 10-fold stratified cross-validation was used to optimize model performance ( Kasew et al ., 2021 ). This approach tested different parameter combinations while preserving the resistant/susceptible class distribution across folds, ensuring fair and unbiased evaluation ( Sartorius et al ., 2024 ). The models, hyperparameters tuned, tested ranges, and rationale are shown in Table 2 . Random forest was used to assess the impact of each feature on the final prediction of the ML model. This allowed the contribution of each variable to the model’s output to be determined ( Quoc et al ., 2023 ). View this table: View inline View popup Download powerpoint Table 2: Models selected and hyperparameter tuning 2.8 Model evaluation The training set (3 243 records) was partitioned into 10 equal folds, with each fold serving as a validation set once, whereas the remaining nine folds were used for training. Stratification preserved the balance between resistant and susceptible classes to address potential imbalances. Performance was evaluated on both the cross-validated training set and the hold-out test set (811 records) using a suite of metrics derived from confusion matrices ( Table 3 ). View this table: View inline View popup Download powerpoint Table 3: Evaluation metrics based on positive and negative predictions of a model Where TP (true positive) was the number of resistant strains predicted to be resistant, TN (true negative) was the number of sensitive strains predicted to be sensitive, FP (false positive) was the number of sensitive strains predicted to be resistant, and FN (false negative) was the number of resistant strains predicted to be sensitive. 3. Results 3.1 Distribution of specimens and bacterial isolates A total of 4 054 clinical specimens were examined to establish the distribution of specimen types and isolated pathogens. The most common specimens examined were pus samples (43.76%), followed by urine samples (32.04%), stool samples (7.89%), and blood samples (6.19%). The other specimens included swabs (3.43%), catheters (2.69%), high vaginal swabs (2.34%), pleural fluid (1.09%), cerebrospinal fluid (0.39%), septic wound swabs (0.12%), and ascitic fluid (0.05%) samples ( Table 4 ). View this table: View inline View popup Table 4: Frequency of specimen types and pathogens. The most common isolated pathogens were lactose-fermenting coliforms (LFC) (26.34%), S. aureus (22.67%), K. pneumoniae (11.00%), and E. coli (8.83%) ( Table 4 ). V. cholarae represented 6.24% of isolates, reflecting potential cholera outbreaks, while the occurrence of non-LFCs (8.34%) and P. aeruginosa (4.79%) reflected the presence of opportunistic and potentially drug-resistant pathogens. Other less commonly isolated microorganisms included P. mirabilis (4.32%), Streptococcus spp. (3.53%), C. freundii (0.86%), Enterobacter spp. (1.01%), and C. albicans (0.10%). Analysis of 4 054 AST results from 874 patients at GPH revealed varied resistance patterns across gender, age group, season, and antibiotic type. High resistance was observed in 45.5% of the isolates from males and 41.0% of the isolates from women. For the age group, resistance ranged from 35.1% in children aged 0–4 years to 45.4% in adults aged 65 years and older. Notable differences in resistance were recorded between seasons, with resistance occurring in 45.4% of the isolates in winter and 41.6% of the isolates in summer ( Table 5 ). View this table: View inline View popup Table 5: A summary of the distribution of resistance and susceptibility among various demographics and antibiotic treatments Resistance varied across antibiotics, with bacitracin (91.7%) being the highest, followed by ampicillin (80%) and nitrofurantoin (75.7%). Moderate resistance was observed for tetracycline (72.1%), amoxicillin (63.8%), and cefotaxime (57.7%). Low resistance levels were recorded for antibiotics such as amikacin (17.5%) and imipenem (7.7%). The trend highlights widespread resistance to older, commonly used antibiotics, while last-resort drugs retain greater efficacy. 3.2 Performance and evaluation of ML models The ML models evaluated on the test set of 811 isolates (20%) demonstrated effectiveness in predicting resistance patterns for surveillance purposes. The SVM achieved the highest accuracy (72.1%), F1 score (0.764), and AUC-ROC (0.791), followed closely by logistic regression, which achieved a precision of 0.731 and log loss of 0.548. Gradient boosting excelled in recall (0.880), but had the lowest specificity (0.433), whereas random forest and KNN recorded balanced metrics ( Table 6 ). View this table: View inline View popup Download powerpoint Table 6: Comparative performance metrics of random forest, logistic regression, SVM, gradient boosting, and KNN models for predicting antimicrobial resistance Confusion matrices for the five ML models (random forest, logistic regression, SVM, Gradient boosting, and KNN) provided further insight into the classification performance for the resistance and susceptible classes. The SVM demonstrated the strongest balance, with a false negative rate of 20.22% (1 – recall), and a false positive rate of 37.96% (1 – specificity). Logistic regression achieved a moderate performance with a false positive rate of 25.00% and a false negative rate of 35.98%. Gradient boosting exhibited the highest recall (88%) and had a notably high false positive rate of 56.66% due to its low specificity. In contrast, random forest and KNN showed false positive rates of 23.91% and 27.17%, respectively, and false negative rates of 38.81% and 41.64% ( Figure 4 ). The ROC curve compares the classification performance of five models. SVM achieved the highest AUC (0.7886), followed closely by Logistic regression (0.7877), whereas KNN had the lowest (0.7532) ( Figure 5 ). Download figure Open in new tab Figure 5: ROC-AUC curves for five machine learning models. 3.3 Analysis of key predictors and drivers of antimicrobial resistance Feature importance analysis for the random forest model revealed that antibiotic type was the most influential predictor of resistance patterns, followed by the bacterial isolate type, with detailed importance scores ordered from highest to lowest ( Figure 6 ). Download figure Open in new tab Figure 6: Feature importance from the random forest model. Resistance rates differed significantly across hospital wards ( Figure 7 ). The stroke ward had the highest median resistance rate of about 75%, whereas the postnatal ward had the lowest resistance rate of about 25%. Wards such as ICU and OPD displayed high variability, with resistance rates reaching 100% in some cases, highlighting areas of concern for targeted surveillance. Download figure Open in new tab Figure 7: Resistance rates (%) of pathogens against antimicrobials across 21 wards and staff The resistance rates (%) of various pathogen classes to a specific antimicrobial agent are shown in Figure 8 . Providencia spp. exhibited the highest resistance rate, reaching approximately 70%, while Corynebacterium spp. showed the lowest resistance at about 20%. Download figure Open in new tab Figure 8: Resistance rates (%) of various pathogens to antimicrobial agents Antibiotic effectiveness against bacterial isolates, assessed using a 50% vulnerability threshold, varied across ( Figure 9 ). Imipenem demonstrated the highest effectiveness at about 90%, while Bactrim was the least effective at about 10%. Most other drugs, including Ceftriaxone and Tetracycline, fell below the effectiveness threshold, indicating widespread AMR. Download figure Open in new tab Figure 9: Effectiveness of antibiotics against isolated pathogens based on sensitivity tests and a vulnerability threshold of 50% Resistance patterns across bacterial isolates and antibiotics ( Figure 10 ) underscore high resistance of P. mirabilis to most antibiotics (100% to amoxicillin and Ceftriaxone), in contrast to low resistance of C. albicans (0% to chloramphenicol). Specific rates are detailed in the heatmap ( Figure 10 ). Download figure Open in new tab Figure 10: Resistance patterns of antibiotics across pathogens MDR prevalence ( Figure 11 ) was highest among Streptococcus spp. followed by K. pneumoniae (about 500 cases each), whereas Providencia spp. exhibited the fewest MDR cases at about 50. The distribution of multidrug-resistant cases reveals that S. aureus accounted for the highest burden, followed closely by Klebsiella spp. These two pathogens were the dominant contributors to antimicrobial resistance in the dataset. Other notable contributors included E. coli , Pseudomonas spp., indicating that both Gram-negative and Gram-positive bacteria were significantly involved in the resistance patterns. In contrast, pathogens such as Shigella spp., Salmonella spp., and Enterococcus spp. showed notably fewer cases of resistance. Download figure Open in new tab Figure 11: Multidrug resistance counts of various pathogens 4. Discussion The resistance rates recorded in this study, at 45.5% for males and 41.1% for females, are consistent with international trends of higher resistance in males ( Salehi et al ., 2025 ). This may be attributed to increased exposure to healthcare facilities or specific risk factors, including catheter use ( Salloum et al ., 2020 ). The findings of this study are supported by a study conducted in Ethiopia, where similar gender-based disparities were observed, with resistance levels of 48.2% in males and 42.5% in females. This is largely attributed to a higher hospitalisation rate among the male population ( Intahphuak et al ., 2020 ). The differences could be reflective of general health-seeking behaviours, where males are more often exposed to invasive interventions or antibiotic regimens ( Emgård et al ., 2021 ). Notably, although our findings align with those of some previous studies, there has been research that reported conflicting results, suggesting that gender may not necessarily affect resistance to certain infections, such as uropathogenic infections ( Sié et al ., 2019 ). The results also indicate resistant patterns across age groups, ranging from 35.1% in children (0–4 years) to 45.5% in older individuals (≥65 years), which aligns with reports of older patients having up to 50% resistance rates in the South African population ( Chaw et al ., 2018 ). This trend can be attributed to comorbidities and cumulative lifetime exposure to antibiotics ( Pringle et al ., 2017 ), and it calls for tailored antibiotic stewardship strategies that target age-specific risk factors ( Bansal et al ., 2019 ). Additionally, Kosai et al . (2017) confirm that the prevalence of antibiotic resistance increases with age. In contrast, there is evidence from some previous studies indicating that antibiotic resistance in younger patients could be worse due to their higher exposure to antibiotics in the earlier phases of life ( Wu et al ., 2021 ). Our results showed significant variations among seasons in resistance rates, with 45.4% resistance in winter and 41.6% in summer, aligning with the findings of previous studies that suggest higher resistance rates in colder months ( Fischer and Bild, 2019 ). The influence of seasonal dynamics on infection indicates a complex interplay between environmental dynamics and resistance levels. Therefore, high vigilance during winter seasons is paramount ( Shapiro et al ., 2019 ). Additionally, this requires the development of integrated surveillance systems that can detect and respond to seasonal trends in resistance ( Abeleda et al ., 2023 ). The extreme differences in resistance across wards, increasing to 75% in stroke wards from just 25% in postnatal wards, are a cause for concern. The differences could be due to the severity of illness in patients and the level of exposure to antibiotics across various clinical settings (Zahedani et al ., 2018). Previous studies have also reported that specialty wards and ICUs have higher levels of resistance, likely due to longer hospital stays and more extensive use of antibiotics ( Wu and Zhao, 2023 ). These findings call for targeted intervention in high-risk environments, such as improved infection control practices in stroke wards and ICU environments ( Sholeh et al ., 2021 ). Our results indicate alarming antimicrobial resistance patterns recorded in this study, for example, 100% resistance of P. mirabilis and intermediate susceptibility of K. pneumoniae (75% were susceptible). Comparable AMR levels have been reported in Kenya, with up to 95% resistance of P. mirabilis to multiple common antibiotics, highlighting the global threat posed by this pathogen, often augmented by plasmid-mediated resistance genes ( Fischer and Bild, 2019 ; Van Bijnen et al ., 2015 ). Additionally, class-level trends for antibiotic effectiveness, characterised by 90% effectiveness of imipenem vs 10% effectiveness of Bactrim, echo worldwide reports of Bactrim resistance exceeding 70% in most areas ( Bryce et al ., 2018 ). These findings underscore the urgent need to update treatment guidelines based on real-time data regarding resistance patterns ( Goldstein et al ., 2018 ). Analysis of the distribution of MDR cases revealed that S. aureus and K. pneumoniae accounted for the highest burden of resistance, emerging as the dominant contributors to the AMR profile. These were followed by other notable pathogens, such as E. coli and Pseudomonas spp., highlighting the importance of both Gram-positive and Gram-negative bacteria in influencing resistance patterns. In contrast, fewer MDR cases were recorded among Shigella spp., Salmonella spp., and Enterococcus spp., suggesting a much lower resistance burden within these groups. The concentration of resistance in a few high-priority organisms signals the need for focused surveillance and intervention strategies. These findings corroborate those of previous studies in Zimbabwe, such as Mhondoro et al . (2019) , which reported high resistance rates in E. coli and S. aureus , and high resistance to last-resort antibiotics, such as fluoroquinolones and carbapenems, in Salmonella spp., P . aeruginosa , and A. baumannii . These trends emphasize the pressing necessity of implementing comprehensive AMR surveillance systems to support timely treatment decisions and strengthen antimicrobial stewardship efforts, particularly in resource-limited settings. Resistance patterns in this study revealed that resistance rates were highest for bacitracin (91.7%), ampicillin (80%), and nitrofurantoin (75.7%), while resistance was moderate for tetracycline (72.1%), amoxicillin (63.8%), and cefotaxime (57.7%). In contrast, resistance to amikacin (17.5%) and imipenem (7.7%) was low, suggesting their continued effectiveness. These trends align with previous findings from Zimbabwe, where high resistance was reported against widely used antibiotics such as ampicillin, penicillin, and ciprofloxacin ( Magwenzi et al ., 2017 ; WHO, 2018; Llor and Bjerrum, 2015). The 2007 AMR situational analysis identified these drugs among the most prescribed, with resistance increasing between 2012 and 2017 due to overuse (WHO, 2018; Llor and Bjerrum, 2015). The rising resistance to augmentin and cephalosporins might reflect increased reliance on these drugs as alternatives, a trend also reported in South Korea, where third-generation cephalosporin resistance was linked to ESBL-producing bacteria ( Kim et al ., 2017 ). Our findings confirm a notable presence of ESBL-producing Enterobacteriaceae, consistent with the findings of Magwenzi et al . (2017) , who reported 52% ESBL carriage in Harare. Such organisms hydrolyze beta-lactam antibiotics, reducing treatment efficacy ( Peirano and Pitout, 2019 ). Consequently, treatments often require costly fourth-generation cephalosporins and aminoglycosides ( Ventola, 2015 ). Fortunately, Carbapenem resistance remains low (<1%) as indicated in this study and previously in Harare ( Magwenzi et al ., 2017 ), affirming their status as effective last-line agents despite limited accessibility in low-income settings. The accurate prediction of resistance patterns enhances the strength of AMR surveillance. The better performance of the SVM model with an accuracy of 72.08% supports previous findings in favour of SVM efficiency in predicting antimicrobial resistance ( Bryce et al ., 2018 ). In contrast, gradient boosting’s high recall (0.880) and low specificity (0.433) suggest that sensitivity must be traded off against the risks of potential overpredictions in clinical practice ( Binsted and McNally, 2024 ). These findings are in agreement with current research pointing out issues of class imbalance in machine learning algorithms applied in clinical environments ( Wu et al ., 2021 ). Our findings have significant implications for AMR surveillance in Gweru and similar resource-limited environments as they validate CDC recommendations for targeted interventions ( Alrebish et al ., 2022 ). They could inform on the possibility of using ML models, such as SVM, in active surveillance systems, iteratively adjusting empirical therapies as successfully practiced in UK hospitals ( Khalid et al ., 2023 ). The striking ineffectiveness of currently used antibiotics, such as Bactrim, indicates the need for updated clinical guidelines, resonating with the WHO global AMR action plan. 5. Limitations and future work This study had limitations related to its retrospective secondary analysis study design. The dataset was obtained from routinely collected laboratory reports; records of key clinical information and distinction between hospital-acquired and community-acquired infections were not available. It was also impossible to ascertain whether the reported AMR was primary or secondary. Therefore, resistance trends were therefore used over the entire study population without stratification by acquisition site or patient visit history. In addition, while data were collected at a public sector tertiary hospital, they are not inherently representative of AMR patterns at nearby rural clinics or private health facilities, which could compromise the generalizability of the results to other health facilities within the Midlands Province or Zimbabwe at large. Further research should involve expanding AMR surveillance efforts within Zimbabwean hospitals using linked networks to generate comprehensive datasets necessary to maximize ML model efficiency. The incorporation of genetic sequencing to characterize resistance mechanisms will be critical in maximizing predictive models, supported by affordable technologies. Comprehensive longitudinal research is required to track evolving AMR patterns and inform adaptive treatment policies. 6. Conclusion This study, based on clinical and demographic data from 874 patients, demonstrated the potential of machine learning to enhance antimicrobial resistance surveillance in Gweru, Zimbabwe. The SVM model was developed and achieved an accuracy of 72.08%, precision of 73.25%, and recall rate of 79.78%, outperforming the random forest, logistic regression, gradient boosting, and KNN models. Antibiotic use was identified as the primary driver of AMR, with hospital ward, patient age, and pathogen type as significant factors, revealing high MDR in S. aureus and low MDR in Shigella spp. and Serratia marcescens . These findings highlight the value of machine learning-driven tools for improving AMR surveillance and stewardship in resource-limited settings, where traditional surveillance methods are weak. This framework can inform national AMR strategies by enabling low-cost and scalable surveillance solutions. Ethical approval Ethical approval for this study was granted by the institutional review boards and ethics committees of the Gweru Provincial Hospital. The patient records that formed the main dataset were completely anonymized so that the participant identities were not recognizable. The board waived the requirement for informed consent because of the retrospective nature of the study. Data were kept confidential, securely stored and only used for the purpose of this study. Administrative approval to access the data was granted by the same body. Authors’ contributions View this table: View inline View popup Download powerpoint Conflict of interest The authors declare that they have no competing interests. No financial or non-financial relationships exist that could be perceived to influence the results or interpretation of this study. Funding sources No external funding was received for research design, data collection, analysis, interpretation, and manuscript preparation. Data availability Due to patient confidentiality and ethical restrictions, the dataset is not publicly available. De-identified data may be accessed upon reasonable request. References ↵ Abeleda , M. A. , Peña , I. , Salenga , R. , Capule , F. , Nacabu-an , S. M. and Nala , P . ( 2023 ). ‘ Antimicrobial Consumption and Resistance of Restricted Antibiotics in a Level III Government Hospital ’. Acta Medica Philippina . doi: 10.47895/amp.vi0.8056 . OpenUrl CrossRef ↵ Adedeji , R. , Agboeze , T. , Akomolafe , A. and Daramola , O . 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