A bagging ensemble machine learning algorithm achieved 97% accuracy on the Framingham and Indicators of Heart Disease datasets, outperforming individual classifiers for heart disease prediction.
Does an ensemble learning approach with PCA and LDA improve heart disease prediction accuracy compared to individual classifiers?
Advanced ensemble machine learning algorithms, particularly bagging, combined with feature extraction and balancing techniques, achieved up to 97% accuracy in predicting heart disease.
Heart disease is a global health concern with a high mortality rate , necessitating early, accurate, and reliable prediction methods for effective prevention and control. In this research, we combine principal component analysis and linear discriminant analysis to reduce dataset complexity and enhance the performance of heart disease classification models by selecting the most relevant features. We address the class imbalance by employing two balancing techniques: oversampling and the synthetic minority oversampling technique, which ensures a more representative dataset, leading to more accurate predictions. Our study develops a novel ensemble approach, utilizing a combination of random forest , support vector machine , K-nearest neighbors, logistic regression , decision tree , and Gaussian naive Bayes to significantly improve heart disease prediction accuracy. Furthermore, we implement advanced ensemble learning techniques, such as Stacking, Bagging, Voting, and Boosting, to achieve early and precise prediction of heart disease. The performance evaluation is conducted on three datasets: Cleveland Heart Disease, Framingham Heart Disease, and Indicators of Heart Disease Dataset (2020), ensuring a robust validation of our methods. The results demonstrate that the voting ensemble machine learning algorithm (VEMLA) achieved 92% accuracy on the Cleveland Heart Disease dataset, while the bagging ensemble machine learning algorithm (BEMLA) achieved 97% accuracy on both the Framingham Heart Disease and Indicators of Heart Disease (2020) datasets. Notably, the proposed BEMLA consistently outperformed other methods, showcasing its superiority in heart disease prediction. This study contributes a comprehensive and effective approach to heart disease diagnosis, outperforming individual classifiers and providing valuable insights for practical clinical applications. • Combines DT, RF, LR, NB, SVM, and KNN models for heart disease prediction. • Applies oversampling and SMOTE to handle imbalanced datasets, improving accuracy. • Uses PCA and LDA for feature extraction, reducing data size and enhancing results. • Ensemble methods like Bagging 97% accuracy and Voting 92% boost model performance.
Rabbi et al. (Sat,) conducted a other in Heart disease. Ensemble machine learning algorithms (Bagging, Voting) with PCA, LDA, and SMOTE vs. Individual classifiers was evaluated on Prediction accuracy. A bagging ensemble machine learning algorithm achieved 97% accuracy on the Framingham and Indicators of Heart Disease datasets, outperforming individual classifiers for heart disease prediction.
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