Title: Identification of stroke using deepnet machine learning algorithm

Authors: Abdulwahhab Alshammari; Noorah Atiyah; Hanoof Alaboodi; Riyad Alshammari

Addresses: Health Informatics Department, College of Public Health and Health Informatics, King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, KSA; King Abdullah International Medical Research Centre (KAIMRC), Ministry of National Guard Health Affairs, Riyadh, KSA ' Faculty of Health Sciences, Simon Fraser University, Burnaby British Columbia, Canada ' King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS), King Abdullah International Medical Research Centre (KAIMRC), Ministry of National Guard Health Affairs, Riyadh, KSA ' National Centre for Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Riyadh, KSA

Abstract: Stroke is a disease that impacts individuals of all races, genders, and backgrounds. To combat the rising prevalence of the disease, the creation of accurate diagnostic tools is paramount. This paper uses two machine learning models, deepnet and decision tree, to assess the health record data from the Ministry of National Guard Health Affairs, Saudi Arabia. Deepnet outperformed the decision tree in accurately predicting stroke and stroke mimic. Deepnet achieved an accuracy of 92.35%, while decision tree achieved 90.8%. Future application of machine learning on stroke and stroke mimic diagnosis holds great potential in public health reform, patient empowerment, and minimising healthcare burden. This paper proposes building a national centralised semi-detection stroke data management framework to create a national platform in the diagnoses, acute, and long-term treatment of stroke.

Keywords: stroke; machine learning; identification.

DOI: 10.1504/IJMEI.2023.133083

International Journal of Medical Engineering and Informatics, 2023 Vol.15 No.5, pp.416 - 429

Received: 31 Jan 2021
Accepted: 26 Jun 2021
Published online: 01 Sep 2023 *

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