TYPHOID DIAGNOSIS SYSTEM USING MACHINE LEARNING ALGORITHM
Abstract
Typhoid fever remains a significant public health issue in developing countries, where early and accurate diagnosis is essential for treatment and control. Traditional methods of diagnosing typhoid can be time-consuming and may lack sensitivity. In this project, we developed a Typhoid Diagnosis System using machine learning algorithms to aid in accurate and timely detection. The system utilizes patient symptoms and medical data to predict the likelihood of typhoid infection. This report details the design, implementation, and evaluation of the system, employing algorithms such as Decision Trees, Support Vector Machines (SVM), and Random Forest. The results demonstrated promising accuracy rates, supporting the feasibility of machine learning for typhoid diagnosis.
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