MACHINE LEARNING MODELS FOR EARLY DETECTION AND PUBLIC HEALTH MANAGEMENT OF TUBERCULOSIS
TL;DR
The Gradient Boosting Machine (GBM) model demonstrated superior performance on the test set, and its high specificity may reduce unnecessary confirmatory testing, while its core predictors offer biological insights into TB-associated inflammatory processes.
Abstract
Background: Tuberculosis (TB) continues to be one of the most pressing health issues worldwide, and in 2024, 10.6 million new cases were reported, resulting in 1.3 million deaths. However, the early and accurate diagnosis is crucial to limit transmission but the existing means are expensive, time consuming, and impractical in resource-limited environments. The objective of the present study was to build a quick and low-cost diagnostic model based on the use of blood biomarkers routinely available in the laboratory. Tuberculosis (TB) continues to be one of the more deadly diseases, particularly in the developing world. This study is aimed at developing a prototype solution where primary signs, symptoms and risk factors of TB would be identified at an early stage and machine learning (ML) predictive algorithms would be applied to them. Methods: Data for 818 confirmed TB cases and 2,618 healthy controls were analyzed in a retrospective manner. Since the dataset is imbalanced, the ROSE technique was used to balance the training set. Seven machine learning algorithms were trained, and feature selection was done using LASSO regression and forward selection to determine the most predictive variables. SHAP analysis was used to increase interpretability of the model, and the final predictive model was implemented as an interactive Shiny web application to make it easier to be used in the clinic. Result: The Gradient Boosting Machine (GBM) model demonstrated superior performance on the test set, achieving an area under the curve (AUC) of 0.821, specificity of 85.7%, and sensitivity of 64.9%. SHAP analysis highlighted platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and platelet distribution width (PDW) as the most influential predictors. Adjusting the classification threshold to 0.24 improved sensitivity to 82.6% while maintaining acceptable specificity (58.9%), underscoring the model’s potential utility as a screening tool. The accompanying Shiny application enhances accessibility and practical deployment in clinical settings. Conclusion: This study presents a robust and interpretable GBM-based diagnostic model leveraging routine hematological parameters to provide a rapid, low-cost TB screening tool suitable for resource-constrained environments. The model’s high specificity may reduce unnecessary confirmatory testing, while its core predictors offer biological insights into TB-associated inflammatory processes. The interactive web application facilitates integration into clinical workflows, supporting early detection and improved TB management.