A Symptom Analysis Model for the Detection of Multiple Disease Using Ensemble Machine Learning Algorithm

EasyChair Preprint no. 11062

Abstract

The primary goal of this application is to identify five unique diseases based on symptom analysis. This application will be based on R programming language, Python, and React, with project data stored on the cloud-based Railway database server. The current illness detection method has various flaws, including low accuracy, long processing times, and exorbitant prices. This project aims to overcome these challenges by analysing symptom data and detecting diseases with high accuracy and speed using powerful machine learning techniques. The suggested method outperforms the existing approach in various ways, including faster disease diagnosis, higher accuracy rates, cheaper costs, and improved accessibility. Because the system is hosted on a cloud server, users can access it from any location. Furthermore, the system is user-friendly, with an intuitive interface that allows users to readily input their symptoms.

Keyphrases: Accuracy, Detecting Disease, Python, React, Symptom Analysis

@Booklet{EasyChair:11062,
  author = {S Veena and Aniket Kumar and Santhosh R Kumar and Aditya Swaroop},
  title = {A Symptom Analysis Model for the Detection of Multiple Disease Using Ensemble Machine Learning Algorithm},
  howpublished = {EasyChair Preprint no. 11062},

  year = {EasyChair, 2023}}