Title: Implementation of Heart Disease Prediction System using Data Mining Technique
Year of Publication: 2019
Publisher: International Journal of Computer Systems (IJCS)
ISSN: 2394-1065
Series: Volume 06, Number 3, July 2019
Authors: Samradhi Mittal, Prof. Shraddha Kumar


Samradhi Mittal, Prof. Shraddha Kumar, "Implementation of Heart Disease Prediction System using Data Mining Technique", In International Journal of Computer Systems (IJCS), pp: 40-45, Volume 6, Issue 3, July, 2019. BibTeX

	author = {Samradhi Mittal, Prof. Shraddha Kumar},
	title = {Implementation of Heart Disease Prediction System using Data Mining Technique},
	journal = {International Journal of Computer Systems (IJCS)},
	year = {2019},
	volume = {6},
	number = {3},
	pages = {40-45},
	month = {July}


The data mining and it’s techniques are enable us to use the historical examples to predict the upcoming events. That kind of techniques is known as the predictive data modeling. The proposed work is an application of predictive data mining system. The risk of heart disease is increases much frequently. The increase rate of heart disease requires understanding the patterns. In this context the proposed study is focused on heart disease prediction system. In this context a data mining based techniques is proposed. The proposed technique involves the use of association rules for predicting the heart disease risk. Thus first a data is collected for experimentation and system design. The obtained dataset contains the attributes and the class labels as the prediction outcome. This dataset is first preprocessed, during the preprocessing the data is transformed into a range of values. In further after conversion of data the outlier detection process is taken place. Therefore to measure the outlier and their removal the regression analysis based method is used. Finally the outcome of the outlier detection and removal process the data is used with the apriori algorithm for generation of the association rules. These recognized rules are used for classification of test dataset. Using the outcome of test data classification the performance of the proposed model is computed and compared against the traditionally available ARM based algorithm. The result shows the proposed technique is efficient and accurate as compared to the traditional ARM algorithm.


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Heart disease prediction system, data mining algorithm, association rule mining, ARM algorithm, outlier detection, regression analysis.