Machine Learning Equipped Web-Based Disease Prediction and Recommender System

Worldwide, several cases go undiagnosed due to poor healthcare support in remote areas. In this context, a centralized system is needed for effective monitoring and analysis of the medical records. A web-based patient diagnostic central platform store history predict possible disease based on current symptoms experienced by ensure faster accurate diagnosis. Early prediction can help users determine severity take quick action. The proposed utilizes machine learning-based classification techniques data set acquired from National Centre Disease Control (NCDC). K-nearest neighbor (K-NN), random forest Naive Bayes approaches are utilized, an ensemble voting algorithm also where each classifier assigned weights dynamically confidence. equipped with recommendation scheme recommend type tests existing patient, so that necessary precautions be taken. database ensures preserved there transparency system. tampering into prevented giving no “updation” rights once diagnosis created.