Feature selection and risk prediction for patients with coronary artery disease using data mining
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Published on: October 11, 2018
Nashreen Md Idris1, Yin Kia Chiam2, Kasturi Dewi Varathan3
1Department of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
Insights
Predicting coronary artery disease (CAD) risk is crucial for early treatment. This study identifies key patient features using machine learning to improve CAD risk prediction models, achieving over 90% AUC.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Coronary artery disease (CAD) is a leading global cause of mortality.
- Early risk prediction of CAD enables timely interventions and reduces mortality rates.
- Existing studies often use extensive datasets, potentially leading to reduced prediction model performance due to data redundancy.
Purpose of the Study:
- To identify significant features for building accurate CAD risk prediction models.
- To enhance the performance of machine learning models for CAD risk stratification.
- To address data imbalance issues in cardiovascular datasets.
Main Methods:
- Feature selection using Chi-squared test, recursive feature elimination, and Embedded Decision Tree.
- Application of Synthetic Minority Over-sampling Technique (SMOTE) to handle imbalanced datasets.
- Development and evaluation of eight machine learning algorithms on Acute Coronary Syndrome (ACS) datasets from NCVD Malaysia.
Main Results:
- Identification of significant features crucial for CAD risk prediction.
- Implementation of SMOTE effectively addressed dataset imbalance.
- Top-performing prediction models achieved an Area Under the Curve (AUC) exceeding 90%.
Conclusions:
- Feature selection significantly improves the performance of CAD risk prediction models.
- Machine learning models, when optimized with feature selection and SMOTE, demonstrate high accuracy in predicting CAD risk.
- The findings support the development of more effective, data-driven tools for early CAD detection and management.
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%. Graphical abstract.
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