Conference Abstract | Volume 8, Abstract NACNDC/19JASH035 (Oral 1D) | Published: 20 Nov 2025
Daphne Agwang1,&, David Guwatudde1, ChrisBalwanaki1, Grace Banturaki2, Barbara Castelnuovo2, Joseph Musaazi2,3
1Department of Epidemiology and Biostatistics, School of Public Health, College of Health Sciences, Makerere University, P.O BOX 7062, Kampala, 2Infectious Diseases Institute, College of Health Sciences, Makerere University, P.O Box 7062, 3University of Antwerp, Faculty of Medicine and Health Science, Belgium
&Corresponding author: Daphne Agwang, Department of Epidemiology and Biostatistics, School of Public Health, College of Health Sciences, Makerere University, P.O BOX 7062, Kampala, Email: daphnehopeagwang@gmail.com
Received: 25 Aug 2025, Accepted: 20 Oct 2025, Published: 20 Nov 2025
Domain: Infectious Disease Epidemiology
Keywords: Cardiovascular disease, HIV, Machine learning, Risk prediction, SHAP
©Daphne Agwang et al. Journal of Interventional Epidemiology and Public Health (ISSN: 2664-2824). This is an Open Access article distributed under the terms of the Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article: Daphne Agwang et al., Cardiovascular disease risk prediction among people living with HIV in Uganda: A comparison of traditional statistical and machine learning models. Journal of Interventional Epidemiology and Public Health. 2025;8(ConfProc6):035 https://doi.org/10.37432/JIEPH-CONFPRO6-00035
The objective of our study was to develop and compare the performance of traditional statistical models and machine learning (ML) algorithms for cardiovascular diseases (CVDs) risk prediction among people living with HIV (PLHIV) in Uganda, and identify key predictors using interpretable AI tools.
Data from 1,000 adult PLHIV followed for 10 years at the Infectious Diseases Institute, Kampala were analyzed. CVD was defined as a composite of stroke, myocardial infarction, heart failure, and peripheral vascular disease. Models included Logistic regression, Cox proportional hazards, and ML algorithms (Decision Tree, Random Forests, Support Vector Machine, XGBoost, and Soft Voting Ensemble). Class imbalance was handled using the SMOTE, and hyperparameters were tuned through a grid search with 10-fold cross-validation. Performance was assessed using AUC, sensitivity, specificity, precision, recall and F1-Score. Feature importance was examined with SHAP and permutation analyses.
Of the participants, 61.7% were female, median age was 55 years, median ART duration 231.7 months, and median CD4 count was 504 cell/µl. Most had a viral load of <400copies/Ml (91.3%), self-employed (58.3%) and married/cohabiting (50.8%). During follow-up 31.8% developed CVDs, more commonly among those ≥55years (34.7% vs 28.3%, P=0.03), with hypertension (36.3% vs 28.6, P=0.01) or diabetes (43.2% vs 30.4%, P=0.06). No differences seen by gender, employment or substance use. The median ART duration before CVDs onset was 232.3 months. Traditional models showed moderate predictive ability (AUC < 0.65), Soft Voting Ensemble achieved best performance (AUC: 0.812) and feature importance highlighted, CD4 count, hypertension, diabetes, alcohol usage, age, marital and employment status as key predictors.
Nearly one-third of the participants developed CVDs. ML ensemble models substantially outperformed Traditional models for CVD risk prediction, while interpretable ML identified clinically relevant predictors. These findings support integration ML tools into HIV care to strengthen CVD risk stratification and prevention.
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