Generic Disease Prediction using Symptoms with Supervised Machine Learning

Authors(4) :-Ashish Kailash Pal, Pritam Rawal, Rahil Ruwala, Prof. Vaibhavi Patel

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.

Authors and Affiliations

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

Naive Bayes, Decision Tree, Random Forest

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Publication Details

Published in : Volume 5 | Issue 2 | March-April 2019
Date of Publication : 2019-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 1082-1086
Manuscript Number : CSEIT1952297
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Ashish Kailash Pal, Pritam Rawal, Rahil Ruwala, Prof. Vaibhavi Patel, "Generic Disease Prediction using Symptoms with Supervised Machine Learning", International 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
Journal URL : http://ijsrcseit.com/CSEIT1952297

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