Ventricular Vision: AI-Driven Heart Disease Detection with CNN-SVM-Based Classification

This study builds upon the CNN-SVM model as a sound diagnostic method for identifying five distinct heart diseases. We utilize the effectiveness of the deep learning procedures of various CNNs and the ability of the SVMs, which, in the end, might be customized and optimized to solve particular cardiac diseases. Two classifications examined are atrioventricular block, arrhythmia, bradycardia, tachycardia, second-degree heart block, and annexation. The appraisal of CNN-SVM model performance accomplished through various parameters was detailed. The precision of the correct classes arrayed from 94.89% to 97.00%, the recall ranged between 93.70% and 98.91%, and the F1-scores were also high, extending from 94.87% to 97.25%. A significant enhancement in the precision and accuracy of the model was registered, which demonstrated statistics close to 0.99 for Class 1, 0.98 for Classes 2 and 3, and 0.99 for Classes 4 and 5, highlighting its high performance. Various types of averages were used to evaluate the model's effectiveness. The superiority of the macro average in accuracy, recall, and F1-score was demonstrated. It is calculated for all classes equally. The results were 96.14%, 96.20%, and 96.16%. We applied the weighted average (taking into account class imbalance) and got 96.15% for the straight as well as for the recall and the F1-score. The micro average, which selects the best from among all classes with identical scores, is kept at 96.15% for all tips, showing a well-suited classifier.