An effective heart disease detection and severity level classification model using machine learning and hyperparameter optimization methods
Document Type
Article
Publication Date
1-1-2022
Abstract
Cardiovascular disease (CVD) is the leading cause of death worldwide. A Machine Learning (ML) system can predict CVD in the early stages to mitigate mortality rates based on clinical data. Recently, many research works utilized different machine learning approaches to detect CVD or identify the patient's severity level. Although these works obtained promising results, none focused on employing optimization methods to improve the ML model performance for CVD detection and severity-level classification. This study provides an effective method based on the Synthetic Minority Oversampling Technique (SMOTE) to handle imbalance distribution issue, six different ML classifiers to detect the patient status, and Hyperparameter Optimization (HPO) to find the best hyperparameter for ML classifier together with SMOTE. Two public datasets were used to build and test the model using all features. The results show that SMOTE and Extra Trees (ET) optimized using hyperband achieved higher results than other models and outperformed the state-of-the-art works by achieving 99.2% and 98.52% in CVD detection, respectively. Also, the developed model converged to 95.73% severity classification using the Cleveland dataset. The proposed model can help doctors determine a patient's current heart disease status. As a result, it is possible to prevent heart disease-related mortality by implementing early therapy.
Keywords
Heart, Predictive models, Support vector machines, Classification tree analysis, Feature extraction, Radio frequency, Prediction algorithms, CVD detection, Severity classification, Hyperparameter optimization, Extra trees, Imbalance, Hyperband
Divisions
sch_ecs
Funders
Ministry of Higher Education through the Fundamental Research Grant Scheme,FRGS/1/2020/ICT02/UM/02/2
Publication Title
IEEE Access
Volume
10
Publisher
Institute of Electrical and Electronics Engineers
Publisher Location
445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA