Cardiac disorder classification: an efficient novel deep Kronecker neural network with sand cat swarm optimisation algorithm for feature selection
by Meghavathu S.S. Nayak; Hussain Syed
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 47, No. 2, 2025

Abstract: Diagnosing a disease takes time and requires highly technical methods. These days, predicting and diagnosing cardiovascular disease (CVD) is crucial to lowering the death rate and catching them in early stages. Prior research employed machine learning (ML) techniques for disease prediction; however, adequate attention should have been paid to feature identification through appropriate methods for selecting features. This research introduced a novel deep learning (DL)-based deep Kronecker neural network (DKNN) for CVD classification. Essential features are extracted using the DenseNet-201 approach, and feature selection techniques help highlight the most important traits while reducing diagnosis costs. Therefore, the sand cat swarm optimisation (SCSO) method is used to identify the most relevant features for diagnosing heart disease. Furthermore, the imbalanced data problem is resolved, and overfitting is decreased through cycle generative adversarial network (CGAN) based data augmentation. Differentiating from other approaches, the proposed approach obtains above 99% accuracy, precision, recall, and F1-score.

Online publication date: Mon, 03-Mar-2025

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