Machine Learning-Based Stroke Prediction: A Critical Analysis

Stroke is a critical public health issue that frequently has long-term impairment and negative effects. Devising innovative methods that enable timely and accurate identification and intervention is crucial. In this regard, machine learning (ML) and deep learning (DL) approaches of artificial intelligence (AI) play a crucial role in reducing the incidence of strokes. This study systematically analyzed articles from 2012 to 2022 using the PRISMA Method. PRISMA is a tool that facilitates researchers' access to an online platform for self-directed learning. The cumulative quantity of articles gathered for ten years reached 1405 from five databases. However, only 79 relevant articles were used for identification. The main objective was to provide a thorough taxonomy that classifies using and implementing machine learning approaches for stroke prediction. The results of this experiment confirm that machine-learning techniques have a great deal of potential for accurate stroke prediction. Nevertheless, challenges such as biased data and algorithms and the need for models that can be adjusted to accommodate various demographics and healthcare systems continue to exist. It is essential to recognize the need for additional research projects that thoroughly explore potential data biases, algorithmic biases, and the generalizability of models across various demographics and healthcare systems. More research is necessary to further the literature on the complete assessment of machine learning models in precisely forecasting stroke occurrences.