Title: CNN-enabled transfer learning and cosine rat swarm optimisation for classification of heart disease

Authors: K. Saravanan; B. Sasikumar

Addresses: Department of Electronics and Communication Engineering, Infant Jesus College of Engineering, Tirunelveli – Thoothukudi National Highway, Kamarajar Nagar, Keelavallanadu, Tamil Nadu – 628851, India ' Department of Computer Science Engineering, Infant Jesus College of Engineering, Tirunelveli – Thoothukudi National Highway, Kamarajar Nagar, Keelavallanadu, Tamil Nadu – 628851, India

Abstract: This research introduces the cosine rat swarm optimisation (CRSO)-based transfer learning (TL) model for the classification of heart disease by using medical data. Originally, the input medical data are accumulated and then the image is pre-processed by using the min-max normalisation. Then, the feature fusion is done using Matusita similarity measures considering the deep maxout network (DMN). Thereafter, the borderline – synthetic minority over-sampling technique (SMOTE) oversampling model is used to augment the data. Then, the classification of heart disease is carried out by using a convolution neural network (CNN) with transfer learning wherein the CNN is used with the hyperparameters from the trained models like deep batch-normalised eLU AlexNet (DbneAlexnet). Here, the training of DbneAlexnet is done using the CRSO algorithm. The proposed method achieved an accuracy of 91.7%, with a true negative rate (TNR) of 91% and a true positive rate (TPR) of 91.8%.

Keywords: hyperparameter tuning; transfer learning; min-max normalisation; deep maxout network; DMN; Matusita similarity.

DOI: 10.1504/IJDMB.2025.148960

International Journal of Data Mining and Bioinformatics, 2025 Vol.29 No.4, pp.402 - 426

Received: 23 Feb 2024
Accepted: 04 Sep 2024
Published online: 06 Oct 2025 *

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