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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

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