AI-Powered Mortality Prediction for HIV/AIDS Patients on ART in Nigeria

AI-Powered Mortality Prediction for HIV/AIDS Patients on ART in Nigeria | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 20 August 2025 V1 Latest version Share on AI-Powered Mortality Prediction for HIV/AIDS Patients on ART in Nigeria Authors : Okechukwu J. Obulezi 0000-0002-7753-1868 , Mohamed A. F. Elbarkawy , Mmesoma P. Nwankwo , EbereChukwu Q. Chinedu , Chinyere P. Igbokwe , Gaber Sallam Salem Abdalla , and John Abonongo [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175573340.08993238/v1 286 views 188 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This study compares three Machine Learning (ML) algorithmsâĂŤlogistic regression, random forest, and gradient booster classificationâĂŤand three deep learning (DL) algorithmsâĂŤartificial neural network, tabular model, and long short-term memory network (LSTM)âĂŤto predict mortality rates among HIV/AIDS patients in Nigeria. The research utilized a large electronic medical records database, merging clinical and demographic data like CD4 count, viral load, age, and ART duration. Following ethical approval from the Nigerian Ministry of Health, the data was preprocessed to address a significant 1:40 class imbalance using SMOTE oversampling and standardization. Feature engineering was also performed, including the encoding of categorical variables. Key findings indicate that ’Current_Age,’ ’MaritalStatus,’ and ’Duration on ART (Days)’ significantly impacted mortality prediction, while ’Sex’ features had minimal actual influence. SHAP analysis was used to interpret feature contributions. Although the ML and ANN models were explainable, the LSTM network achieved perfect scores (accuracy, precision, recall, F1 score of 1), suggesting potential overfitting despite controls like Loss-Based Stopping and a dropout layer. The tabular embedding model, with an accuracy of 0.9695, highlighted that not all metrics are suitable for highly imbalanced scenarios. The study emphasizes the critical importance of data integrity for policy relevance and suggests future research should use different validation techniques to ensure model reliability. Supplementary Material File (pmhivaids (1).pdf) Download 2.66 MB Information & Authors Information Version history V1 Version 1 20 August 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords deep learning hiv/aids prediction machine learning mortality rates Authors Affiliations Okechukwu J. Obulezi 0000-0002-7753-1868 Nnamdi Azikiwe University View all articles by this author Mohamed A. F. Elbarkawy Imam Muhammad Ibn Saud Islamic University View all articles by this author Mmesoma P. Nwankwo Nnamdi Azikiwe University View all articles by this author EbereChukwu Q. Chinedu Nnamdi Azikiwe University View all articles by this author Chinyere P. Igbokwe Lovely Professional University School of Computer Science and Engineering View all articles by this author Gaber Sallam Salem Abdalla Imam Muhammad Ibn Saud Islamic University View all articles by this author John Abonongo [email protected] C K Tedam University of Technology and Applied Sciences Faculty of Mathematical Sciences View all articles by this author Metrics & Citations Metrics Article Usage 286 views 188 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Okechukwu J. Obulezi, Mohamed A. F. Elbarkawy, Mmesoma P. Nwankwo, et al. AI-Powered Mortality Prediction for HIV/AIDS Patients on ART in Nigeria. Authorea . 20 August 2025. DOI: https://doi.org/10.22541/au.175573340.08993238/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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