Disease Prediction Android Application using Machine Learning

Authors

  • Sneha Rajendra Bedse
  • Menika Mahendra Prasad
  • Vrushali Pankaj Thakur

Keywords:

Naive Bayes algorithm, symptoms, android app, machine learning, disease prediction

Abstract

The rapid proliferation of internet technology and handled devices has opened up new avenues for an online healthcare system. There are instances where online medical help or healthcare advice is easier and faster to grasp than real world help. People often feel reluctant to go to hospital or visit doctors for minor symptoms. However, in many cases, these minor symptoms may trigger into a major health hazard. As online wellbeing counsel is effectively reachable, it very well may be an extraordinary head start for clients. Besides, existing on the web medical services frameworks experience, the ill effects of an absence of unwavering quality and precision. An android application is developed for users for easy portability, configuring and being able to access remotely where doctors cannot reach easily. This application analyzes the symptoms provided by the user as input and gives the most accurate probable disease output to user. This application also looks forward by providing information like description about predicted disease and measures to be taken by users of a particular predicted disease. Expectation is finished by carrying out the Naive Bayes Classifier.

Downloads

Published

2021-06-12

Issue

Vol. 8 No. 1 (2021)

Section

Research Articles

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.