Title: A novel method to predict stroke using deep learning approach

Authors: Swagata Sarkar; A. Jayashree; R. Thirumurugan; C. Sivakumaran

Addresses: Artificial Intelligence and Data Science Department, Sri Sairam Engineering College, West Tambaram, Chennai, India ' Department of Computer Science and Business Systems, Veltech Multitech Dr. Rangarajan and Dr. Sakunthala Engineering College, Chennai-62, Tamil Nadu, India ' Department of Electronics and Communication Engineering,, SA Engineering College, Chennai, India ' Machine Learning Engineer, Photon Technologies, Chennai, 600017, India

Abstract: Strokes remain the main cause of major impairment that lasts for lengthy period of time and ranks as the third greatest cause of mortality overall in the USA. The ability to accurately anticipate a stroke is very useful for facilitating earlier intervention as well as treatments. Several studies have concentrated on determining the chance of a heart attack; on the other hand, only few have investigated the possibility of a brain attack. Various machine learning techniques are being created to estimate the chance of a cerebral infarction. We present a data-driven classifiers deep neural networks (ResNet) for diagnosing strokes predicated on 12-leads ECG information. The quality of the model, which has been fine-tuned to perfection, allows us to achieve a training correctness of 99.99% and an accuracy rate of 85.82%. The findings imply that ECG is a viable adjunct tool for diagnosing stroke.

Keywords: machine learning; stroke prediction; CNN; deep learning; ResNet.

DOI: 10.1504/IJMEI.2025.148637

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.5, pp.444 - 452

Received: 30 Sep 2022
Accepted: 19 Dec 2022
Published online: 17 Sep 2025 *

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