Analyzing Existing Algorithms and Identifying Gaps in Brain Stroke Detection
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Abstract
One of the global issues that matters most right now is healthcare. The primary cause of death globally is brain stroke. A valuable contribution to medicine is the early prediction and identification of brain stroke events. Numerous factors, including BMI (body mass index), age, sex, family history, gender, smoking status, hypertension, and so on, are linked to brain stroke deaths. While forecasting heart illness has received a great deal of interest in the medical community, predicting a brain stroke has received less attention. This study's primary goal is to evaluate various previously published research publications and select the most effective machine learning methods for brain stroke prediction for our next projects. It was shown that mortality rate and functional outcomes are the expected outcomes for the majority of the study work done after analyzing the various machine learning techniques used for stroke predictions and after accounting for the previously published studies. The techniques that were used most commonly were LR, DTC, RFC, SVM, KNN.
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