Enhanced feature selection and ensemble learning for cardiovascular disease prediction: hybrid GOL2-2 T and adaptive boosted decision fusion with babysitting refinement - Praveen, S. Phani; Hasan, Mohammad Kamrul; Abdullah, Siti Norul Huda Sheikh; Sirisha, Uddagiri; Tirumanadham, N. S. Koti Mani Kumar; Islam, Shayla; Ahmed, Fatima Rayan Awad; Ahmed, Thowiba E.; Noboni, Ayman Afrin; Sampedro, Gabriel Avelino; Yeun, Chan Yeob; Ghazal, Taher M. (Frontiers in Medicine, 2024)
Cardiovascular disease is a major global health concern that necessitates accurate and efficient diagnostic tools. In order to reduce death rates and improve the forecast accuracy of cardiac disease, this work introduces a novel machine learning approach. To address data-related challenges, the approach that is being suggested makes use of Multivariate Imputation by Chained Equations (MICE), Interquartile Range (IQR) outlier detection, and Synthetic Minority Over-sampling Technique (SMOTE). Hybrid (2-Tier Grasshopper Optimization with L2 regularization) named as GOL2-2T is a new hybrid feature selection method that we provide in this work. This method enhances the feature selection process by combining the Grasshopper Optimization Algorithm (GOA) with L2 regularization. We use Adaptive Boosted Decision Fusion (ABDF) ensemble learning with a babysitting algorithm. Our model has 83.0% accuracy and 84.0% balanced F1 score, significantly better than previous methods. Our heart disease prediction method is successful using accuracy, recall, and AUC score. This research helps build reliable diagnostic methods that allow doctors to detect cardiovascular illness early and treat patients effectively.