Hybrid brave-hunting optimisation for heart disease detection model with SVM coupled deep CNN

This research proposes a novel hybrid optimisation method called brave-hunting optimisation (BHO) inspired by lion optimisation (LO) and coyote optimisation (CO).The BHO algorithm is employed to fine-tune the SVM parameters, enhancing its classification performance.Simultaneously, a deep CNN model extracts complex and informative features from medical data.The combined approach capitalises on the strengths of both optimisation techniques to create a robust and accurate model for heart disease diagnosis.The performance evaluation of our model is conducted using comprehensive metrics, which achieve an accuracy of 94.89%, an F1-score of 94.48%, a precision of 94.48%, and a recall of 94.58% for a 90 TP.In the context of a ten k-fold evaluation, achieved 94.78% accuracy, 94.36% F1-score, 94.55% precision, and 94.13% recall.