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tools for the general public, creates a significant gap in stroke risk awareness

and management. Addressing this gap is essential for timely interventions and

preventive measures to reduce the incidence of strokes and improve public
health outcomes.

However, the feasibility of using machine learning algorithms for predicting

stroke risk in a system that is accessible to the general public needs to be

investigated in order to develop more accessible and user-friendly tools for
predicting stroke risk (Tajdini et al., 2022.). By doing so, it is hoped that the

general public will have more accurate and convenient access to stroke risk
prediction tools, which can lead to better outcomes for patients and reduced

healthcare costs (Virani et al., 2021).

Without embracing the potential of machine learning in stroke risk assessment

tools, individuals may lack access to the most accurate and user-friendly
resources for understanding their stroke risk. This could lead to missed

opportunities for early intervention and prevention, ultimately resulting in
increased stroke incidence, associated disability, and overall healthcare costs

(Virani et al., 2021). Inaccurate risk predictions might cause missed chances

for early intervention, resulting in more strokes and higher healthcare costs.
Also, the lack of self-awareness and limited access to traditional tools could

worsen the gap in stroke risk awareness and management. As a result, high risk
individuals may not get timely medical help or take preventive measures,

which could increase stroke-related health issues and deaths. Finally, failing to

investigate the feasibility of using machine learning algorithms for predicting
stroke could hinder the development of more accessible and user-friendly

tools. Consequently, the general public may continue to face barriers in
accessing accurate and personalized stroke risk assessments, limiting their

ability to take proactive measures to reduce their risk of stroke and improve

their overall health outcomes.

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