Abstract—Machine Learning (ML), often viewed as a cutting-edge technology best suited for qualified specialists, presents limited access for other physicians and scientists in the medical profession. In this work, we provide a new, sophisticated, and highly successful technology for medical applications, especially cardiac diagnostics. We propose a novel advanced hybrid optimization model with two essential parts. Initially, we apply a high-performance hybrid resampling technique for feature engineering and pre-processing. This approach, which combines Synthetic Minority Oversampling Technique Edited Nearest Neighbors (SMOTEENN) with Neighborhood Cleaning Rules (NCL), addresses class imbalance in the data. We developed a complex hybrid optimization model that incorporates hyper-parameter optimization, advanced Application Programming Interface (API) functions, and a super-learner ensemble model to enhance diagnosis accuracy in cases where datasets lack balance. Furthermore, we developed high-performance prediction models using sophisticated Support Vector Machines (SVMs). We show that, with re-sampled Cardiovascular Disease (CVD) data, the advanced hybrid optimization model attained an astounding accuracy of 98%. By comparison, an advanced SVM model obtained 96% accuracy, while an advanced deep learning model produced 95.5% accuracy. Our new sophisticated hybrid optimization machine learning models may significantly improve physicians’ interpretation of ML results. This strategy could make it easier to apply AI methods on a large scale in the clinic, which would eventually raise patient outcomes and diagnostic accuracy.
 
Keywords—Cardiovascular Disease (CVD), Neighborhood Cleaning Rules (NCL), Synthetic Minority Oversampling Technique Edited Nearest Neighbors (SMOTEENN), hybrid advanced models, optimization, Application Programming Interface (API) function


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