IFN-γ and IL-6 as Key Predicting Biomarkers for Active TB Among PLWH: Results from Four Machine Learning Methods

Purpose: Tuberculosis remains a major cause of mortality in people living with HIV (PLWH), yet early diagnosis remains challenging. This study aimed to identify novel biomarker combinations and develop machine learning models, and to predict active TB in PLWH in a random and a chronological subset. Patients and Methods: We enrolled 760 PLWH with pulmonary symptoms. Demographic and clinical data and cytokine profiles were analyzed. Participants were first randomly split into training and validation sets. Subsequently, the whole dataset was re-analysed using the first 609 records as the training set and subsequent 151 records as the test set. Four models were developed with 10-fold cross-validation, incorporating feature selection and hyperparameter optimization. Model performance was assessed through ROC-AUC, sensitivity, specificity, and variable importance analysis. Results: