Common human diseases prediction using machine learning based on survey data

TL;DR

This paper surveys common disease prediction using machine learning on survey data, analyzing symptoms and employing various classifiers to identify the most effective model for disease detection.

Contribution

It introduces a survey-based dataset for disease prediction and compares multiple classification algorithms to determine the best performing model.

Findings

Part classifier outperforms others in accuracy

Survey data effectively used for disease prediction

Multiple algorithms evaluated for performance

Abstract

In this era, the moment has arrived to move away from disease as the primary emphasis of medical treatment. Although impressive, the multiple techniques that have been developed to detect the diseases. In this time, there are some types of diseases COVID-19, normal flue, migraine, lung disease, heart disease, kidney disease, diabetics, stomach disease, gastric, bone disease, autism are the very common diseases. In this analysis, we analyze disease symptoms and have done disease predictions based on their symptoms. We studied a range of symptoms and took a survey from people in order to complete the task. Several classification algorithms have been employed to train the model. Furthermore, performance evaluation matrices are used to measure the model's performance. Finally, we discovered that the part classifier surpasses the others.