STUDY AND REPORTING OF STATISTICAL ANALYSIS TO DETECT TUBERCULOSIS USING MACHINE LEARNING APPROACH FOR BETTER UNDERSTANDING AND ENHANCING DECISION MAKING

This paper provides a theoretical review of the use of machine learning (ML) techniques for tuberculosis (TB) detection.TB remains a significant public health challenge worldwide, and accurate diagnosis is critical for effective treatment and control.Chest X-rays (CXRs) are commonly used for TB diagnosis, but interpretation can be challenging, particularly in areas with a high prevalence of TB.ML techniques, including traditional ML models and deep learning models such as convolutional neural networks (CNNs), have shown promising results for TB detection from CXRs.This paper discusses the different ML techniques used for TB detection from CXRs, the challenges and limitations of these techniques, and the potential future directions for research in this area.The review highlights the potential of ML techniques to improve TB diagnosis accuracy and efficiency and provides insights for researchers and practitioners working in this field.