Manuscript received May 12, 2024; revised June 24, 2024; accepted July 4, 2024; published September 25, 2024
Abstract—The World Health Organization recommends routine immunological and virologic monitoring for all patients with Human Immunodeficiency Virus (HIV) infection. However, viral load and lymphocyte T CD4 (LTCD4) count analysis requires sophisticated equipment and qualified human resources. This creates a financial burden, especially in limited resource settings. Thus, there is a need for alternative approaches. One such alternative is machine learning (ML), which offers a more cost-effective solution. In this study, five highly optimized data-driven models for LTCD4 prediction were designed based on popular ML techniques: support vector machine (SVM), random forest (RF), logistic regression (LR), artificial neural networks (ANNs), and naive Bayes (NB). To guarantee the robust performance of the proposed algorithms, we meticulously scrutinized the optimal approach for constructing models. Furthermore, we analyzed the predictive capabilities of LTCD4 according to multiple thresholds of the total lymphocyte count. Moreover, an imbalance-aware strategy to overcome the aforementioned issue was adopted using the synthetic minority oversampling technique. The cutoff points for the number of lymphocytes “1100” had the best performance in predicting the LTCD4 count. SVMs, RF, NB, LR, and ANNs provided an area under the curves of 97%, 93.2%, 90%, 92.01%, and 93%, respectively. SVMs achieved better results in predicting LTCD4 in all metrics. The results offer novel perspectives on LTCD4 forecasting, presenting opportunities to enhance initiatives aimed at developing web-based systems. These systems could alleviate the financial burden associated with measuring LTCD4 in patients with HIV infection, particularly in resource-constrained settings.
Keywords—HIV, CD4 T lymphocyte count, total lymphocyte count, machine learning
Cite: Saad Lamjadli, Oumayma Ouedrhiri, Ikram Souli, Zouhair Elamrani Abou Elassad, Oumayma Banouar, Safa Machraoui, Moulay Yassine Belghali, Raja Hazime, Noura Tassi, Said Raghay, Brahim Admou, "Prediction of CD4 T-Lymphocyte Count via Machine Learning for HIV-positive Patients," International Journal of Machine Learning vol. 14, no. 3, pp. 97-106, 2024.
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).