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Enhancing heart disease prediction with stacked ensemble and MCDM-based ranking: an optimized RST-ML approach.

T Ashika1, G Hannah Grace1

  • 1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology Chennai, Chennai, India.

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Summary

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.

Keywords:
correlation analysisgridsearchCVmachine learningmulti-criteria decision-makingrough set theorystacking classifier

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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.