Generic Disease Prediction using Symptoms with Supervised Machine Learning

Authors

  • Ashish Kailash Pal  Department of CSE, Parul University, Vadodara, Gujarat, India
  • Pritam Rawal  Department of CSE, Parul University, Vadodara, Gujarat, India
  • Rahil Ruwala  Department of CSE, Parul University, Vadodara, Gujarat, India
  • Prof. Vaibhavi Patel  Department of CSE, Parul University, Vadodara, Gujarat, India

DOI:

https://doi.org//10.32628/CSEIT1952297

Keywords:

Naive Bayes, Decision Tree, Random Forest

Abstract

Data Mining and Machine Learning plays most inspiring area of research that become most popular in health organization. It also plays a vital part to uncover new patterns in medicinal science and services association which thusly accommodating for all the parties associated with this field. This project intend to form a diagnostic model of the common diseases based on the symptoms by using data mining technique such as classification in health domain. In this project, we are going to use algorithms like Random forest, Naive Bayes which can be utilized for health care diagnosis. Performances of the classifiers are compared to each other to find out highest accuracy. This also helps us to find out persons who are affected by the infection. The test based on the outcomes of the diseases.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-04-30

Issue

Volume 5, Issue 2, March-April-2019

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Ashish Kailash Pal, Pritam Rawal, Rahil Ruwala, Prof. Vaibhavi Patel, " Generic Disease Prediction using Symptoms with Supervised Machine Learning, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.1082-1086, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT1952297