Identification of stroke using deepnet machine learning algorithm Online publication date: Fri, 01-Sep-2023
by Abdulwahhab Alshammari; Noorah Atiyah; Hanoof Alaboodi; Riyad Alshammari
International Journal of Medical Engineering and Informatics (IJMEI), Vol. 15, No. 5, 2023
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.
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