Title: An intelligent method for predicting cardiac disease based on PSO-convolutional neural network

Authors: M. Balamurugan; P. Blessed Prince

Addresses: Department of Computer Science Engineering, The Kavery Engineering College, Mecheri, Salem – 636453, India ' Department of Computer Science Engineering, Presidency University, Bangalore – 560064, India

Abstract: Cardiovascular disease (also known as CVD) is one of the primary contributors to both morbidity and death. The present state of the art in artificial intelligence plays a significant part in the process of aiding medical professionals in the diagnosis of a variety of disorders. A hybrid framework is proposed for the diagnosis of cardiovascular illnesses by analysing medical voice data. Eleven datasets comprising 14,416 numerical characteristics may be produced by using the method that has been suggested. From the datasets that are produced as a consequence, numerical and graphical characteristics are extracted. In the third layer, numerical data is provided to five separate machine learning (ML) techniques and graphical characteristics are transmitted to convolutional neural networks (CNNs), with transfer learning utilised to choose the best suited configurations. From a benchmark dataset, the PSO-CNN technique that was presented obtains an accuracy of 96.78%.

Keywords: cardiovascular; PSO-CNN; optimisation; machine learning.

DOI: 10.1504/IJMEI.2025.148640

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.5, pp.463 - 475

Received: 08 Dec 2022
Accepted: 10 Feb 2023
Published online: 17 Sep 2025 *

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