Enhanced Feature Selection and Ensemble Learning for Cardiovascular Disease Prediction: Hybrid GOL2-2T and Adaptive Boosted Decision Fusion with Babysitting Refinement Provisionally Accepted

S Phani Praveen¹ Mohammad Kamrul Hasan² Chan Yeob Yeun^3* Siti Norul Huda Sheikh Abdullah² Shayla Islam⁴ Uddagiri Sirish¹ N S Koti Mani Kumar Tirumanadham⁵ Fatima Rayan Awad Ahmed⁶ Thowiba Elawad Ahmed⁷ Ayman Afrin Noboni⁸ Gabriel Avelino Sampedro⁹ Taher M Ghazal³

• ¹Prasad V. Potluri Siddhartha Institute of Technology, India
• ²National University of Malaysia, Malaysia
• ³Khalifa University, United Arab Emirates
• ⁴UCSI University, Malaysia
• ⁵Sir C.R.Reddy College of Engineering, India
• ⁶Prince Sattam Bin Abdulaziz University, Saudi Arabia
• ⁷Imam Abdulrahman Bin Faisal University, Saudi Arabia
• ⁸Medical College for Women and Hospital, Bangladesh
• ⁹University of the Philippines Open University, Philippines

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.