Classification Technique for Heart Disease Prediction in Data Mining
Mohini Chakarverti1, Rajiva Ranjan Divivedi2
1Mohini Chakarverti, Research Scholar, IEC College of Engineering and Technology, Greater Noida (Uttar Pradesh), India.
2Rajiva Ranjan, Assistant Professor, Divivedi, IEC College of Engineering and Technology, Greater Noida (Uttar Pradesh), India.
Manuscript received on 13 June 2019 | Revised Manuscript received on 09 July 2019 | Manuscript Published on 17 July 2019 | PP: 63-66 | Volume-8 Issue-1C2 May 2019 | Retrieval Number: A10120581C219/2019©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The difficult information is analyzed through an approach named data mining while prediction analysis approach is used for the prediction of information on the basis of input data suite. Currently, a lot of methods have been implemented for prediction analysis. In the proposed study, clustering and classification of the input information for heart disease forecasting is executed with the help of k-means clustering algorithm and SVM (support vector machine) classification model on the basis of prediction analysis methods. The back propagation algorithm along with k-means clustering algorithm is applied for the clustering of information. These algorithms support to enhance the precision of prediction analysis. A heart disease data suite obtained from the UCI repository is used for judging the performance of proposed algorithm. This data suite comprises total 76 features. But, whole tests require a subset of 14 features. Particularly, Cleveland database particularly is utilized by the machine learning researchers throughout the tests. A comparison between proposed study and earlier method (using arithmetic mean) is performed in terms of certain parameters such as accuracy, error recognition rate and execution time.
Keywords: SVM, KNN, Heart Disease Prediction.
Scope of the Article: Data Mining