Machine learning equipped web based disease prediction and recommender system

TL;DR

This paper presents a web-based disease prediction and recommendation system using machine learning classifiers and ensemble voting to improve diagnosis speed and accuracy, especially in remote healthcare scenarios.

Contribution

It introduces an ensemble machine learning approach with dynamic weighting for disease prediction and a test recommendation scheme within a centralized web platform.

Findings

Effective disease prediction with ensemble classifiers.

Improved diagnostic accuracy through dynamic weighting.

Enhanced healthcare support in remote areas.

Abstract

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 system is a central platform to store the medical history and predict the possible disease based on the current symptoms experienced by a patient to ensure faster and accurate diagnosis. Early disease prediction can help the users determine the severity of the disease and take quick action. The proposed web-based disease prediction system utilizes machine learning based classification techniques on a data set acquired from the National Centre of Disease Control (NCDC). $K$-nearest neighbor (K-NN), random forest and naive bayes classification approaches are utilized and an ensemble voting algorithm is also proposed where each classifier is assigned weights dynamically based on the prediction confidence. The proposed system is also equipped with a recommendation scheme to recommend the type of tests based on the existing symptoms of the patient, so that necessary precautions can be taken. A centralized database ensures that the medical data is preserved and there is transparency in the system. The tampering into the system is prevented by giving the no "updation" rights once the diagnosis is created.