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

Authors: Pravin M. Tambe; Manish Shrivastava

Addresses: Department of Computer Science and Engineering, Vivekananda Global University, Jagatpura, Jaipur, Rajasthan, 303012, India ' Department of Computer Science and Engineering, Vivekananda Global University, Jagatpura, Jaipur, Rajasthan, 303012, India

Abstract: 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.

Keywords: cardiovascular disease detection; support vector machines; deep convolutional neural network; DCNN; brave-hunting optimisation; BHO; early prediction.

DOI: 10.1504/IJIIDS.2025.143486

International Journal of Intelligent Information and Database Systems, 2025 Vol.17 No.1, pp.92 - 123

Received: 25 Nov 2023
Accepted: 07 Apr 2024
Published online: 23 Dec 2024 *