BACK PROPAGATION WITH SVM CLASSIFIER FOR THE HEART DISEASE PREDICTION

Himani Rani, Dr. Gaurav Gupta

Abstract


The data mining is the technique to analyze the complex data. The prediction analysis is the technique which is applied to predict the data according to the input dataset. In the recent times, various techniques have been applied for the prediction analysis. In this work, the k-means clustering algorithm and SVM (support vector machine) classifier based prediction analysis technique is used for clustering and classification of the input data. In order to increase the accuracy of prediction analysis, the back propagation algorithm is proposed to be applied with the k-means clustering algorithm to cluster the data. The proposed algorithm performance is tested in the heart disease dataset which is taken from UCI repository. There are 76 attributes present within a database. However, a subset of 14 amongst them is required within all the published experiments. Specifically, machine learning researchers have used Cleveland database particularly at all times. The proposed work will also be compared with the existing scheme (using arithmetic mean) in terms of accuracy, fault detection rate and execution time.


Keywords


SVM, Back propagation, Prediction

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References


Yanhui Sun, Liying Fang and Pu Wang, Improved k-means clustering based on Efros distance for longitudinal data, 2016 Chinese Control and Decision Conference (CCDC), Vol. 11, issue 3, pp. 12-23, 2016.

Shunye Wang, Improved K-means clustering algorithm based on the optimized initial centroids, 2013 3rd International Conference on Computer Science and Network Technology (ICCSNT), Vol. 11, issue 3, pp. 12-23, 2013.

PhattharatSongthung and KunwadeeSripanidkulchai, Improving Type 2 Diabetes Mellitus Risk Prediction Using Classification, 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), Vol. 11, issue 3, pp. 12-23, 2016.

Jiawei Han, MichelineKamber, “Data Mining: Concepts and Techniques”, vol. 3, pp. 1-31, 2000.

Ms. Tejaswini U. Mane, “Smart heart disease prediction system using Improved K-Means and ID3 on Big Data”, 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI), vol. 8, issue 11, pp. 123-148, 2017.

SellappanPalaniappan, RafiahAwang, “Intelligent Heart Disease Prediction System Using Data Mining Techniques”, vol. 5, issue 1, pp. 13-28, 2008.

KanikaPahwa, Ravinder Kumar, “Prediction of Heart Disease Using Hybrid Technique For Selecting Features”, 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON), vol. 4, issue 5, pp. 23-48, 2017.

BayuAdhi Tama,1 Afriyan Firdaus,2 Rodiyatul FS, “Detection of Type 2 Diabetes Mellitus with Data Mining Approach Using Support Vector Machine”, Vol. 11, issue 3, pp. 12-23, 2008.

Yu-Xuan Wang, QiHui Sun, Ting-Ying Chien, Po-Chun Huang, “Using Data Mining and Machine Learning Techniques for System Design Space Exploration and Automatized Optimization”, Proceedings of the 2017 IEEE International Conference on Applied System Innovation, vol. 15, pp. 1079-1082, 2017.

ZhiqiangGe, Zhihuan Song, Steven X. Ding, Biao Huang, “Data Mining and Analytics in the Process Industry: The Role of Machine Learning”, 2017 IEEE. Translations and content mining are permitted for academic research only, vol. 5, pp. 20590-20616, 2017.

P. Suresh Kumar and V. Umatejaswi, “ Diagnosing Diabetes using Data Mining Techniques”, International Journal of Scientific and Research Publications, Volume 7, Issue 6, June 2017.

Han Wu, Shengqi Yang, Zhangqin Huang, Jian He, Xiaoyi Wang, “Type 2 diabetes mellitus prediction model based on data mining”, ScienceDirect, Vol. 11, issue 3, pp. 12-23, 2018.

JahinMajumdar, Anwesha Mal, Shruti Gupta, “Heuristic Model to Improve Feature Selection Based on Machine Learning in Data Mining”, 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence), vol. 3, pp. 73-77, 2016.


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