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

• Published in: PloS one Vol. 20; no. 1; p. e0312914
• Main Authors: Hasan, Mahade, Yasmin, Farhana, Hassan, Md. Mehedi, Yu, Xue, Yeasmin, Soniya, Joshi, Herat, Islam, Sheikh Mohammed Shariful
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09.01.2025; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Artificial intelligence; Bayes Theorem; Biology and Life Sciences; Blood vessels; Cardiovascular disease; Cardiovascular diseases; Classification; Computer and Information Sciences; Data mining; Datasets; Decision Trees; Deep learning; Diagnosis; Fatalities; Feature selection; Heart diseases; Heart failure; Humans; Illnesses; Learning algorithms; Machine Learning; Medicine and Health Sciences; Methods; Morbidity; Mortality; Performance evaluation; Performance measurement; Physical Sciences; Prediction models; Recall; Regression analysis; Research and Analysis Methods; ROC Curve; Stroke; Stroke (Disease); Stroke - diagnosis; Support Vector Machine; Support vector machines; Voting; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0312914

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.