Enhancing Sustainable Healthcare: Machine Learning-Based Tuberculosis Detection Using C4.5 Decision Tree
Keywords:
Tuberculosis,C4.5 decision tree, Sustainable healthAbstract
Tuberculosis (TB) remains a global health crisis, particularly in resource-limited regions where diagnostic infrastructure is scarce. While deep learning models dominate recent research, classical machine learning (ML) methods offer interpretability and computational efficiency—critical for low-resource settings. This study presents the first systematic comparison of 13 ML algorithms, including C4.5 decision trees, logistic regression, and ensemble methods, for TB detection using the Shenzhen chest X-ray dataset .The C4.5 decision tree achieved near perfect accuracy (99.78%) and the lowest training time (0.147s), outperforming deep learning alternatives in interpretability and cost-effectiveness. By providing a deployable, low-cost diagnostic tool, this work directly supports the United Nations’ Sustainable Development Goals (SDGs): SDG-3 (reducing TB mortality), SDG-9 (fostering diagnostic innovation), and SDG-10 (bridging healthcare disparities). Our results demonstrate that classical ML can rival complex models in medical diagnostics while remaining accessible to underserved populations
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