This paper is published in Volume-3, Issue-2, 2017
Area
Data Mining
Author
Hinduja .R, Mettildha Mary .I, Ilakkiya .M, Kavya .S
Org/Univ
Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India
Pub. Date
07 April, 2017
Paper ID
V3I2-1431
Publisher
Keywords
Classification, Clustering, Diagnosis, Feature Reduction, Heart Disease, Machine Learning, Optimization.

Citationsacebook

IEEE
Hinduja .R, Mettildha Mary .I, Ilakkiya .M, Kavya .S. CAD Diagnosis Using PSO, BAT, MLR And SVM, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Hinduja .R, Mettildha Mary .I, Ilakkiya .M, Kavya .S (2017). CAD Diagnosis Using PSO, BAT, MLR And SVM. International Journal of Advance Research, Ideas and Innovations in Technology, 3(2) www.IJARIIT.com.

MLA
Hinduja .R, Mettildha Mary .I, Ilakkiya .M, Kavya .S. "CAD Diagnosis Using PSO, BAT, MLR And SVM." International Journal of Advance Research, Ideas and Innovations in Technology 3.2 (2017). www.IJARIIT.com.

Abstract

Coronary artery disease (CAD) is a most common type of heart disease. CAD happen when a blood clot cuts off the heart’s blood supply, causing permanent heart damage. Diagnosis of CAD can be done using angiography, echocardiogram, electrocardiogram, which are complex methods. Therefore, studies are done to predict CAD using machine learning algorithms. This study proposes, feature selection by particle swarm optimization(PSO) and Bat algorithms, clustering using K-means and classification using Multinomial logistic regression (MLR) and support vector machine (SVM) algorithms. This technique is cross checked upon 14 attributes with 303 instances. A benchmark dataset from Cleveland heart disease data is used. The Bat-SVM model achieves the highest prediction accuracy of 97 %. The proposed model has an increased accuracy from the existing systems.
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