Predicting Tuberculosis Effects Through Integration of Machine Learning Models with Environmental Factors and Historical Disease Records
Year: 2026
Type: conference-paper
Abstract: Tuberculosis (TB) is a global issue that requires better and consistent diagnostic and prognostic indices. The purpose of the paper is to evaluate the outcomes of TB building the models of the Decision Tree and Support Vector Machine (SVM) according to the environment, data about patients, and TB history. The simulation findings and time series analysis of the proposed online SVM model were 86% and 0.89 AUC, which was better than the Decision Tree that had$\mathbf{8 2} \boldsymbol{\%}$and$\mathbf{0. 8 5}$AUC. Besides, the feature importance analysis of the Decision Tree demonstrated that such aspects as air quality or temperature variations were presented. Therefore, the results highlight the potential of the combined strategy to enhance the results of TB and direct the work of mass health.
Authors: Ajay Kumar Tiwari, Alok Katiyar
Institution: Galgotias University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7166809802