Advanced Hybrid Machine Learning Model for Accurate Detection of Cardiovascular Disease

Cardiovascular disease (CVD) is one of the foremost reasons behind the death of people worldwide. Prevention and early diagnosis are the only ways to control its progression and onset. Thus, there is an urgent need for a detection model comprising intelligent technologies, including Machine Learning (ML) and deep learning, to predict the future state of an individual suffering from cardiovascular disease by effectively analyzing patient data. This study aims to propose a hybrid model that provides a deep insight into the data under consideration to enhance model accuracy for effectively detecting cardiovascular disease. This current research proposes a hybrid model comprising four stages. In the first stage of the proposed hybrid model, the data imbalance problem is solved using a hybrid sampling technique named Synthetic Minority Oversampling Technique-Edited Nearest Neighbors Rule. In the second stage, the Chi-square is applied as a feature selection method to select the highly relevant features from the records of 1190 with 11 clinical features, curated by combining the 5 most popular datasets, including Long Beach VA, Hungarian, Switzerland, and Statlog (Heart). In the third stage, the preprocessed dataset is passed to a stacking ensemble model comprising three base learners: Random Forest Tree (RFT), K-Nearest Neighbor (K-NN), and AdaBoost classifier and one meta-learner: Logistic Regression (LR), optimized with Grid Search Cross-Validation (GSCV) optimization approach, whose performance is evaluated against individual classifier. In the fourth stage, the performance is evaluated in terms of accuracy, sensitivity, specificity, F1 score, and ROC_AUC score.. The comparative results prove that the proposed hybrid model scored the highest accuracy of 97.8%, 96.15% sensitivity, and 96.75% specificity and 98.6% ROC_AUC score when compared with the existing techniques and models after applying the SMOTE–ENN (for data balancing) and Chi-square (for feature selection) methods for the efficient detection of cardiovascular disease. The implementation results demonstrate that the suggested hybrid model may accurately identify cardiovascular disease among patients. It facilitates the application of robust clinical treatment strategies.