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All Journal Speed - Sentra Penelitian Engineering dan Edukasi JIKO (Jurnal Informatika dan Komputer) Respati International Journal of Engineering, Technology and Natural Sciences (IJETS) Jurnal Layanan Masyarakat (Journal of Public Service) Indonesian Journal of Data and Science SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen International Journal of Artificial Intelligence in Medical Issues
Sumiyatun
Fakultas Teknologi Informasi, Jurusan Sistem Informasi, Universitas Teknologi Digital Indonesia
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Machine Learning-Based Prediction of HIV/AIDS Infection and Treatment Effectiveness: A Clinical Dataset Analysis Agus Aan Jiwa Permana; I Gusti Ngurah Wikranta Arsa; Ahmad Naswin; Sumiyatun
International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 2 (2025): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v3i2.362

Abstract

The early and accurate prediction of HIV/AIDS infection is critical to improving clinical decision-making and ensuring effective patient management. This study presents a comprehensive machine learning-based approach to predict HIV/AIDS infection status and evaluate the effectiveness of antiretroviral treatments using a well-documented clinical dataset from 1996, comprising 2,139 patient records and 34 features. Through rigorous preprocessing, exploratory data analysis, and feature engineering, several new clinically relevant attributes were constructed, such as CD4/CD8 ratios and immunological change metrics. Four machine learning models—Logistic Regression, Support Vector Machine, Random Forest, and Gradient Boosting—were trained and evaluated. Among these, the Gradient Boosting classifier achieved the highest ROC-AUC score of 0.9335, while Random Forest provided strong predictive performance with a ROC-AUC of 0.9180 and was selected for further evaluation due to its model transparency. Key features influencing infection prediction included CD4+ and CD8+ dynamics, baseline immunological levels, and treatment history. Additionally, the study examined treatment effectiveness by analyzing CD4+ cell count responses across different therapy types. The combination of ZDV and ddI emerged as the most effective regimen, improving immune outcomes and lowering infection rates, while ZDV monotherapy showed the least favorable results. This work underscores the potential of machine learning as a clinical decision support tool in HIV/AIDS care and provides data-driven insights into treatment optimization. Future studies should incorporate longitudinal patient data and real-world clinical environments for broader applicability.
Co-Authors Agus Aan Jiwa Permana Ahmad Naswin Aris Wahyu Murdiyanto Aro Mulya Pratama Cuk Subiyantoro Cuk Subiyantoro, Cuk Edi Faizal Edi Faizal Edi Iskandar Femi Dwi Astuti Feriawan Abdullah Hera Wasiati Hera Wasiati I Gede Iwan Sudipa I Gusti Ngurah Wikranta Arsa Ika Arfiani M Ikbal Siami Maulidinnawati, Andi Naswin, Ahmad Nicodemus Mardanus Setiohardjo Prayitna, Adiyuda Pulut Suryati Retyanto Wardoyo Rezania Agramanisti Azdy, Rezania Agramanisti Sudarmanto Sudarmanto Sudarmanto Sudarmanto, Sudarmanto Syam, Rahmat Fuadi Syamsu Windarti Tarigan, Thomas Edyson Thomas Edyson Tarigan Thomas Edyson Tarigan Totok Suprawoto Umar Zaky Yagus Cahyadi Yasinta Marto Enga