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Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 47 - Number 10
Year of Publication: 2012
Chaitrali S. Dangare
Sulabha S. Apte

Chaitrali S Dangare and Sulabha S Apte. Article: Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques. International Journal of Computer Applications 47(10):44-48, June 2012. Full text available. BibTeX

	author = {Chaitrali S. Dangare and Sulabha S. Apte},
	title = {Article: Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {47},
	number = {10},
	pages = {44-48},
	month = {June},
	note = {Full text available}


The Healthcare industry is generally "information rich", but unfortunately not all the data are mined which is required for discovering hidden patterns & effective decision making. Advanced data mining techniques are used to discover knowledge in database and for medical research, particularly in Heart disease prediction. This paper has analysed prediction systems for Heart disease using more number of input attributes. The system uses medical terms such as sex, blood pressure, cholesterol like 13 attributes to predict the likelihood of patient getting a Heart disease. Until now, 13 attributes are used for prediction. This research paper added two more attributes i. e. obesity and smoking. The data mining classification techniques, namely Decision Trees, Naive Bayes, and Neural Networks are analyzed on Heart disease database. The performance of these techniques is compared, based on accuracy. As per our results accuracy of Neural Networks, Decision Trees, and Naive Bayes are 100%, 99. 62%, and 90. 74% respectively. Our analysis shows that out of these three classification models Neural Networks predicts Heart disease with highest accuracy.


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