Infectious Disease Detection and Drugs Recommendation Using Machine Learning

This paper introduces a machine learning system that combines Logistic Regression, Decision Tree, and Random Forest algorithms to analyze patient data, focusing on symptoms and case histories to predict likely diseases and recommend effective treatments. The objective is to improve early disease detection and personalized medication recommendations, reducing side effects and enhancing clinical judgment. Extensive medical data trains these models, enabling integration with electronic health records to support clinical decisions, which strengthens healthcare delivery. This work contributes to the field by providing a model that increases diagnostic accuracy and offers a tailored treatment plan for individual patients, marking a step towards enhanced personalized healthcare.