Coronary artery disease prediction using hidden Markov model based support vector machine

S Omprakash  lowast; M Ravichandran  nbsp

doi:10.17485/IJST/v13i17.20

Article

Coronary artery disease prediction using hidden Markov model based support vector machine

VIEWS 2588
PDF 350

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v13i17.20

Year: 2020, Volume: 13, Issue: 17, Pages: 1703-1713

Original Article

Coronary artery disease prediction using hidden Markov model based support vector machine

S Omprakash^1∗, M Ravichandran²

1 Research Scholar, Department of Computer Science, SRMV College of Arts and Science, Coimbatore, Tamil Nadu, India. Tel.: 9894959107
2 Associate Professor, Department of Computer Science, SRMV College of Arts and Science, Coimbatore, Tamil Nadu, India

∗Corresponding author
S Omprakash
Research Scholar, Department of Computer Science, SRMV College of Arts and Science, Coimbatore, Tamil Nadu, India.
Tel: 9894959107
Email: [email protected]

Received Date:27 March 2020, Accepted Date:08 May 2020, Published Date:10 June 2020

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Background: Medical data classification has become a hot research domain in data mining, but still it faces the increased classification accuracy issues. Methods/Statistical Analysis: Novel Hidden Markov Model based Support Vector Machine (HMM-SVM) is proposed to classify and predict Coronary Artery Disease (CAD). The features are extracted using HMM, and normalized using SVM. Feature Extraction assist the classification algorithm to get better results. HMM-SVM performs classification by extracting the features of Z-AlizadehSani dataset and finally selects the appropriate feature to perform classification. Findings: Z-AlizadehSani dataset holds 303 records with 4 different types of features, which are demographic, symptom and examination, ECG, and laboratory cum echo. For extracting these features and finding hidden information there exists no common algorithm. In HMM-SVM, HMM is applied to extract features by finding the hidden and previous stage values, and SVM is applied to perform classification on extracted features. To analyze the performance of HMM-SVM benchmark performance metrics are utilized. Discriminative performance results of internal validations are high in the task of binary classification (i.e., sensitivity- 98.2%; specificity-97.96%). False Positive Rate of HMM-SVM is entirely low (i.e.,1.87%) when comparing with previous algorithms. HMMSVM holds the classification accuracy as 98.02% and which is the better cum expected results towards the prediction of CAD. Novelty: Detailed analysis indicates HMM-SVM have better effects towards classifying and predicting CAD. Furthermore, care needs to be placed in adhering to ethical principles while utilizing the models that are automated. Future studies should make use of bio-inspired concepts to get even better results.

Keywords: CAD; Classification; SVM; HMM; Markov

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Copyright

© 2020 Omprakash, Ravichandran. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Indian Society for Education and Environment (iSee)