Machine learning, Model development, Prediction tool, Loss to follow-up, HIV, Low-resource setting, Computer applications to medicine. Medical informatics, R858-859.7

Abstract Background Despite the global commitment to ending AIDS by 2030, the loss of follow-up (LTFU) in HIV care remains a significant challenge. To address this issue, a data-driven clinical decision tool is crucial for identifying patients at greater risk of LTFU and facilitating personalized and proactive interventions. This study aimed to develop a prediction model to assess the future risk of LTFU in HIV care in Ethiopia. Methods The study used a retrospective design in which machine learning (ML) methods were applied to the electronic medical records (EMRs) data of adult HIV-positive individuals who were newly enrolled in antiretroviral therapy between July 2019 and April 2024. The data were collected across eight randomly selected high-volume healthcare facilities. Six supervised ML classifiers—J48 decision tree, random forest, K-nearest neighbors, support vector machine, logistic regression, and naïve Bayes—were utilized for training via Weka 3.8.6 software. The performance of each algorithm was evaluated through a 10-fold cross-validation approach. Algorithm performance was compared via the corrected resampled t test (p