Machine Learning-Based Stroke Prediction: A Critical Analysis of Recent Developments

Agus, Byna and Muhammad, Modi Lakulu and Ismail, Yusuf Panessai (2021) Machine Learning-Based Stroke Prediction: A Critical Analysis of Recent Developments. [Karya Dosen UNISM]

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Abstract

Stroke is a critical public health issue that frequently has long-term impairment and negative effects. 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 79 articles from the years 2012 to 2022 using the PRISMA Method. The main objective was to provide a thorough taxonomy that classifies the use and implementation of 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. It is important 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. In order to further the literature on the full assessment of machine learning models in precisely forecasting the occurrences of stroke, more research is therefore necessary.

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