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Disease Prediction System using Data Mining Hybrid Approach

Rahul Patil, Pavan Chopade, Abhishek Mishra, Bhushan Sane, Yuvraj Sargar. Published in Information Sciences.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Rahul Patil, Pavan Chopade, Abhishek Mishra, Bhushan Sane, Yuvraj Sargar

Rahul Patil, Pavan Chopade, Abhishek Mishra, Bhushan Sane and Yuvraj Sargar. Article: Disease Prediction System using Data Mining Hybrid Approach. Communications on Applied Electronics 4(9):48-51, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Rahul Patil and Pavan Chopade and Abhishek Mishra and Bhushan Sane and Yuvraj Sargar},
	title = {Article: Disease Prediction System using Data Mining Hybrid Approach},
	journal = {Communications on Applied Electronics},
	year = {2016},
	volume = {4},
	number = {9},
	pages = {48-51},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


Earlier as well as nowadays also, the doctors are using trial and error approach for predicting the diseases based on clinical investigations available. To predict the diseases is one of the major challenge in past years and today also. There is great need of some system that predicts the diseases early on the basis of available symptoms and patients health. Because of this it will become possible to cure the people from hazardous diseases which may lead the humans to death for e.g. Cancer, AIDS etc. We are a proposing system which is based on combination of different data mining techniques such as clustering, classification etc. that are useful to predict the patient’s disease state. The patient's disease states can be find out by formalizing the hypothesis based on test results and symptoms of the patient before recommending treatments for the prevailing diseases. The basic aim of our system is to assist doctors in diagnosing the patient by analyzing his available data and relevant information.


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Naïve Bayes, symptoms, data mining, database, graph based, partitioning, hierarchical