Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques

2025-01-09

PLoS ONE

AI Summary

To address the need for early and precise identification of cardiovascular and cerebrovascular diseases, this paper proposes an interpretable classification framework integrating feature selection and ensemble learning. Methodologically, it optimizes features via recursive feature elimination combined with correlation analysis, ensembles nine diverse models—including XGBoost, Random Forest, and SVM—and employs grid search with five-fold cross-validation for hyperparameter tuning; an adaptive weighted voting mechanism is further designed to enhance generalizability and robustness. The key contribution lies in the first-time deep integration of feature selection into the multi-model weight assignment process for voting, thereby jointly optimizing predictive performance and model interpretability. Experimental results demonstrate that the XGBoost submodel achieves 99% accuracy, 99% precision, 98% recall, 99% F1-score, and 100% ROC AUC; overall, the proposed framework significantly outperforms existing state-of-the-art methods.

Technology Category

• Machine Learning: Ensemble Methods
• Computer Vision: Interpretability, Explainability, and Transparency
• Humans and AI: Voting

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models to facilitate early detection and intervention. While state of the art work has focused on various machine learning approaches for predicting heart disease, but they could not able to achieve remarkable accuracy. In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. Our approach involved feature selection techniques to identify the most relevant predictors, aimed at refining the models to enhance both performance and interpretability. The models were trained, incorporating processes such as grid search hyperparameter tuning, and cross-validation to minimize overfitting. Additionally, we have developed a novel voting system with feature selection techniques to advance heart disease classification. Furthermore, we have evaluated the models using key performance metrics including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (ROC AUC). Among the models, XGBoost demonstrated exceptional performance, achieving 99% accuracy, precision, F1-Score, 98% recall, and 100% ROC AUC. This study offers a promising approach to early heart disease diagnosis and preventive healthcare.

Problem

• Improving heart disease prediction accuracy using machine learning
• Developing a novel voting system with feature selection techniques
• Evaluating models via performance metrics like accuracy and ROC AUC

Innovation

• Feature selection enhances model performance and interpretability
• Novel voting system improves heart disease classification
• XGBoost achieves 99% accuracy with optimized hyperparameters