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Enhancing heart disease prediction with stacked ensemble and MCDM-based ranking: an optimized RST-ML approach
1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology Chennai, Chennai, India.
Insights
This study introduces an Optimized Rough Set Theory-Machine Learning framework for accurate heart disease prediction. The novel approach enhances diagnostic accuracy and demonstrates scalability across multiple health conditions.
Area of Science:
- Computational biology and bioinformatics
- Machine learning in healthcare
- Data science for medical diagnostics
Background:
- Cardiovascular disease (CVD) remains a primary global cause of mortality, underscoring the need for advanced diagnostic tools.
- Existing diagnostic models often face challenges with high dimensionality and feature redundancy.
- Integrating machine learning with data reduction techniques offers a promising avenue for improved predictive accuracy.
Purpose of the Study:
- To develop and evaluate an Optimized Rough Set Theory-Machine Learning (RST-ML) framework for heart disease (HD) prediction.
- To enhance diagnostic accuracy and reduce overfitting through stacked ensemble models and multi-criteria decision-making.
- To assess the framework's scalability and generalization capabilities on diverse health datasets.
Main Methods:
- Feature selection using Rough Set Theory (RST) to minimize data dimensionality.
- Development of five stacked ensemble models integrating nine machine learning classifiers.
- Model ranking using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with Mean Rank Error Correction (MEREC) weighting.
- Hyperparameter optimization using GridSearchCV, identifying XGBoost (XG) as the optimal classifier.
- Evaluation on heart disease, chronic kidney disease (CKD), obesity, and breast cancer datasets.
- Application of Explainable AI (XAI) for feature importance analysis.
Main Results:
- The Stack-4 ensemble model, utilizing XGBoost, achieved the highest predictive accuracy.
- Explainable AI (XAI) techniques successfully elucidated key features influencing diagnostic predictions.
- The RST-ML framework demonstrated robust performance across multiple datasets, including CKD and breast cancer.
Conclusions:
- The proposed RST-ML framework significantly improves heart disease prediction accuracy.
- The framework exhibits strong scalability and generalization, proving effective for timely diagnosis across various health conditions.
- This approach offers a robust and adaptable solution for medical diagnostics in diverse clinical settings.
Introduction:
Cardiovascular disease (CVD) is a leading global cause of death, necessitating the development of accurate diagnostic models. This study presents an Optimized Rough Set Theory-Machine Learning (RST-ML) framework that integrates Multi-Criteria Decision-Making (MCDM) for effective heart disease (HD) prediction. By utilizing RST for feature selection, the framework minimizes dimensionality while retaining essential information.
Methods:
The framework employs RST to select relevant features, followed by the integration of nine ML classifiers into five stacked ensemble models through correlation analysis to enhance predictive accuracy and reduce overfitting. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) ranks the models, with weights assigned using the Mean Rank Error Correction (MEREC) method. Hyperparameter tuning for the top model, Stack-4, was conducted using GridSearchCV, identifying XGBoost (XG) as the most effective classifier. To assess scalability and generalization, the framework was evaluated using additional datasets, including chronic kidney disease (CKD), obesity levels, and breast cancer. Explainable AI (XAI) techniques were also applied to clarify feature importance and decision-making processes.
Results:
Stack-4 emerged as the highest-performing model, with XGBoost achieving the best predictive accuracy. The application of XAI techniques provided insights into the model's decision-making, highlighting key features influencing predictions.
Discussion:
The findings demonstrate the effectiveness of the RST-ML framework in improving HD prediction accuracy. The successful application to diverse datasets indicates strong scalability and generalization potential, making the framework a robust and scalable solution for timely diagnosis across various health conditions.
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