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accessibility, accuracy, and personalization of risk assessments. This project
aims to develop dashboard and tool, with the ultimate goal of enhancing stroke
prevention efforts and improving public health outcomes.
1.2 Problem Statement
The widespread use of smartphones and the internet has revolutionized the way
individuals’ access and manage their health information. With an increasing
number of people seeking personalized and convenient tools to monitor their
well-being, there is a growing need for accurate and user-friendly tools to
predict and prevent health issues such as stroke. By using the power of
advanced algorithms and data analysis, it can accurately assess an individual's
risk of stroke. It will benefit the society if the tool is easily accessible and
understandable for the general public, without the need for specialized medical
knowledge.
Despite the global burden of stroke, with over 13.7 million instances occurring
annually and affecting a quarter of individuals aged 25 and above (Tajdini et
al., 2022), the current methods for stroke risk assessment available to the
general public primarily rely on traditional tools. These tools can be time-
consuming and may not accurately represent individual risk factors (Virani et
al., 2021). According to Amann (2021), machine learning (ML) is a promising
tool that can enhance our capacity to assess risk and ultimately prevent strokes,
addressing some of the limitations of current risk prediction models. By using
machine learning techniques, there is an opportunity to develop an accurate
and more accessible tool for stroke risk assessment, leading to improved
prediction and prevention efforts for a diverse range of individuals.
Other than that, high-risk individuals often do not recognize their own
defencelessness to stroke, leading them to seek testing primarily when there is
a suspected cerebrovascular disease event (Qiu et al., 2023). This lack of self-
awareness, coupled with the inaccessibility of current stroke risk prediction
2