This paper is published in Volume-5, Issue-3, 2019
Area
Computer Engineering
Author
Omkar Nevase
Co-authors
Prafull Bansode, Rishikesh Nimbalkar, Sanket Yeginwar, Suvarna Pawar
Org/Univ
Sinhgad College of Engineering, Pune, Maharashtra, India
Pub. Date
17 May, 2019
Paper ID
V5I3-1409
Publisher
Keywords
Data Mining, Prediction, Classification, Naïve Bayes

Citationsacebook

IEEE
Omkar Nevase, Prafull Bansode, Rishikesh Nimbalkar, Sanket Yeginwar, Suvarna Pawar. Health prediction system using Data Mining, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Omkar Nevase, Prafull Bansode, Rishikesh Nimbalkar, Sanket Yeginwar, Suvarna Pawar (2019). Health prediction system using Data Mining. International Journal of Advance Research, Ideas and Innovations in Technology, 5(3) www.IJARIIT.com.

MLA
Omkar Nevase, Prafull Bansode, Rishikesh Nimbalkar, Sanket Yeginwar, Suvarna Pawar. "Health prediction system using Data Mining." International Journal of Advance Research, Ideas and Innovations in Technology 5.3 (2019). www.IJARIIT.com.

Abstract

“Health Prediction” system based on predictive modeling, which predicts the disease (probability of disease) of the user on the basis of the symptoms that the user provides as an input to the system. The system analyses the symptoms provided by the user as input and gives the probability of the disease as an output to the user. Disease Prediction is done by implementing the Naïve Bayes Classifier. Naïve Bayes Classifier calculates the probability of the disease. Therefore, the average accuracy of 75% is obtained for disease prediction. The system also provides suggestions of Doctor’s to the user based on the symptoms analyzed. Along with it also provides suggestions of nearby Doctor’s available in the area of Patient. The user can also share his/her medical reports online with the Doctor, based on which doctor can provide treatment to the user. Also, there is chatting facility available through which Doctor and User can interact with each other before taking an appointment or after the treatment also. The Doctor can also manage all the patients’ records online.
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