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

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