Explainable machine learning for predicting clinical outcomes in HIV/TB co-infection: a comparative retrospective study.

ABSTRACT:

Background

HIV/TB co-infection presents substantial public-health challenges, showing greater treatment-failure and mortality rates than tuberculosis alone. Recent advances in machine learning (ML) provide a robust means of identifying high-risk patients early in the disease course.

Methods

This retrospective study enrolled 359 patients co-infected with HIV and TB at a single tertiary-care hospital. We extracted clinical and immunological data. The cohort was subsequently divided into training (0%) and test (0%) subsets, and class imbalance was addressed with the Synthetic Minority Over-sampling Technique (SMOTE). Six ML classifiers-Random Forest, XGBoost, LightGBM, Support Vector Machine, Extra Trees and CatBoost-were trained after grid-search hyper-parameter tuning. Model

SUBMITTER: Sun Q 

PROVIDER: S-EPMC12619449 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature