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Improving an Intelligent Detection System for Coronary Heart Disease Using a Two-Tier Classifier Ensemble
Bayu Adhi Tama1, Sun Im2, Seungchul Lee1
1Department of Mechanical Engineering, Pohang University of Science and Technology, Republic of Korea.
Insights
A novel machine learning approach using a two-tier ensemble effectively detects coronary heart disease (CHD). This advanced model outperforms existing methods in accuracy and AUC, offering a significant contribution to early heart attack detection.
Area of Science:
- Cardiology
- Machine Learning
- Data Science
Background:
- Coronary heart disease (CHD) is a leading global cause of mortality.
- Heart attacks often occur without warning symptoms, necessitating advanced detection methods.
- Traditional diagnostic approaches may lack the precision for early detection.
Purpose of the Study:
- To develop and evaluate a novel machine learning-based method for detecting coronary heart disease (CHD).
- To enhance the accuracy and generalisability of CHD detection models.
- To improve early identification of heart conditions for timely intervention.
Main Methods:
- A two-tier ensemble machine learning architecture was designed, integrating Random Forest, Gradient Boosting Machine, and Extreme Gradient Boosting classifiers.
- Particle Swarm Optimization was employed for feature selection to identify the most significant predictive variables.
- The model was rigorously evaluated on multiple established heart disease datasets (Z-Alizadeh Sani, Statlog, Cleveland, Hungarian) using 10-fold cross-validation.
Main Results:
- The proposed stacked ensemble model demonstrated superior performance compared to individual base classifiers and traditional ensemble methods.
- The model achieved high accuracy, F1-score, and Area Under the Curve (AUC) across diverse datasets.
- Statistical tests confirmed the significance of the performance improvements and the generalisability of the model.
Conclusions:
- The developed two-tier ensemble model offers a robust and accurate solution for coronary heart disease detection.
- This machine learning approach represents a significant advancement over existing methods, enhancing early diagnosis capabilities.
- The findings suggest a considerable contribution to the field of automated cardiovascular disease detection and risk stratification.
Abstract:
Coronary heart disease (CHD) is one of the severe health issues and is one of the most common types of heart diseases. It is the most frequent cause of mortality across the globe due to the lack of a healthy lifestyle. Owing to the fact that a heart attack occurs without any apparent symptoms, an intelligent detection method is inescapable. In this article, a new CHD detection method based on a machine learning technique, e.g., classifier ensembles, is dealt with. A two-tier ensemble is built, where some ensemble classifiers are exploited as base classifiers of another ensemble. A stacked architecture is designed to blend the class label prediction of three ensemble learners, i.e., random forest, gradient boosting machine, and extreme gradient boosting. The detection model is evaluated on multiple heart disease datasets, i.e., Z-Alizadeh Sani, Statlog, Cleveland, and Hungarian, corroborating the generalisability of the proposed model. A particle swarm optimization-based feature selection is carried out to choose the most significant feature set for each dataset. Finally, a two-fold statistical test is adopted to justify the hypothesis, demonstrating that the performance differences of classifiers do not rely upon an assumption. Our proposed method outperforms any base classifiers in the ensemble with respect to 10-fold cross validation. Our detection model has performed better than current existing models based on traditional classifier ensembles and individual classifiers in terms of accuracy, F 1, and AUC. This study demonstrates that our proposed model adds a considerable contribution compared to the prior published studies in the current literature.
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