Revolutionizing Tuberculosis Prediction: A Cutting-Edge Approach

The diversity of human biological systems and lifestyles requires specialized medical treatments in healthcare. In response to this need, this paper proposes a robust AI-based tuberculosis prediction model using Machine Learning (ML). This model utilizes key patient characteristics and symptoms to predict the presence of the disease using treatment data. For a comprehensive study, the dataset was exposed to a number of algorithms, including Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Naive Bayes (NB). Of these, RF emerged as the top performer, with an impressive accuracy rate of 99.98%. This remarkable accuracy demonstrates its ability to accurately predict tuberculosis based on symptoms and important variables. By utilizing this paradigm, healthcare systems can significantly improve their diagnostic capabilities, resulting in accurate and rapid disease diagnosis. Overall, this breakthrough represents a tremendous advancement in healthcare technology by creating a promising pathway for precise disease prediction, enabling timely treatment, and improving all aspects of healthcare outcomes.