Feature selection and risk prediction for patients with coronary artery disease using data mining
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ORIGINAL ARTICLE
Feature selection and risk prediction for patients with coronary artery disease using data mining Nashreen Md Idris 1 & Yin Kia Chiam 1 Yih Miin Liew 4
&
Kasturi Dewi Varathan 2 & Wan Azman Wan Ahmad 3 & Kok Han Chee 3 &
Received: 16 September 2019 / Accepted: 8 September 2020 # International Federation for Medical and Biological Engineering 2020
Abstract Coronary artery disease (CAD) is an important cause of mortality across the globe. Early risk prediction of CAD would be able to reduce the death rate by allowing early and targeted treatments. In healthcare, some studies applied data mining techniques and machine learning algorithms on the risk prediction of CAD using patient data collected by hospitals and medical centers. However, most of these studies used all the attributes in the datasets which might reduce the performance of prediction models due to data redundancy. The objective of this research is to identify significant features to build models for predicting the risk level of patients with CAD. In this research, significant features were selected using three methods (i.e., Chi-squared test, recursive feature elimination, and Embedded Decision Tree). Synthetic Minority Over-sampling Technique (SMOTE) oversampling technique was implemented to address the imbalanced dataset issue. The prediction models were built based on the identified significant features and eight machine learning algorithms, utilizing Acute Coronary Syndrome (ACS) datasets provided by National Cardiovascular Disease Database (NCVD) Malaysia. The prediction models were evaluated and compared using six performance evaluation metrics, and the top-performing models have achieved AUC more than 90%. Keywords Data mining . Prediction model . Classification algorithms . Feature selection . Heart disease prediction . Coronary artery disease
1 Introduction According to the World Health Organization, coronary artery disease (CAD) has stayed in the charts of the global top 10 causes of death for over the past 15 years [1]. Malaysia is not exempted as ischemic heart disease has remained the principal cause of death in 2016 among Malaysian by dominating
* Yin Kia Chiam [email protected] 1
Department of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
2
Department of Information Systems, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
3
Department of Medicine, University Malaya Medical Centre, 50603 Kuala Lumpur, Malaysia
4
Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
13.5% of the chart [2]. Patients with CAD may be presented clinically with stable angina or ACS. ACS is a spectrum of diseases ranging from unstable angina, non–ST-elevation myocardial infarction (NSTEMI) to ST-elevation myocardial infarction (STEMI) depending on the acuteness and severity of the coronary occlusion [3]. Conditions that have the potential to incr
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