A Smart Health Care Decision Support System Using Machine Learning

Abstract

The demand in the contemporary healthcare environment is high. to have fast, easy, and combined diagnostic instruments is. preeminent to the better patient outcomes. This paper presents establishing a smart medical help system that connects the discontinuity between automated symptom analysis and. clinical follow-up. The main component of the system is a powerful Voting. Random Ensemble model which is a summation of predictions. Algorithms used in classifying Forest, Decision Tree and Naive Bayes. disease search on the basis of symptom inputted by users and improved. accuracy and reliability. Built using the MERN (MongoDB, the platform is based on Express, React, and Node.js) stack, which allows it to be scaled. and real time health evaluation interface. A key technical innovation refers to smooth implementation of a GIS-based. specialist locator, makes use of the Google Places API and. Mapping, making use of leaflets to guide users automatically. specialized medical institutions according to the model of ensemble. diagnostic output. System appraisals suggest that the ensemble method is more diagnostic accurate than. single models however, the amalgamation of mapping is also of great significance. lessens the time delay between primary diagnosis and the recognition of pertinent specialized treatment. This end-to-end solution provides a holistic structure of the digital. patient centric healthcare delivery transformation.