Publication: Machine learning-based patient classification system for adults with stroke: A systematic review
Issued Date
2021-01-01
Resource Type
ISSN
17459206
17423953
17423953
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2-s2.0-85121364309
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Mahidol University
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SCOPUS
Bibliographic Citation
Chronic Illness. (2021)
Suggested Citation
Suebsarn Ruksakulpiwat, Witchuda Thongking, Wendie Zhou, Chitchanok Benjasirisan, Lalipat Phianhasin, Nicholas K. Schiltz, Smit Brahmbhatt Machine learning-based patient classification system for adults with stroke: A systematic review. Chronic Illness. (2021). doi:10.1177/17423953211067435 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/78546
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Title
Machine learning-based patient classification system for adults with stroke: A systematic review
Abstract
Objective: To evaluate the existing evidence of a machine learning-based classification system that stratifies patients with stroke. Methods: The authors carried out a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations for a review article. PubMed, MEDLINE, Web of Science, and CINAHL Plus Full Text were searched from January 2015 to February 2021. Results: There are twelve studies included in this systematic review. Fifteen algorithms were used in the included studies. The most common forms of machine learning (ML) used to classify stroke patients were the support vector machine (SVM) (n = 8 studies), followed by random forest (RF) (n = 7 studies), decision tree (DT) (n = 4 studies), gradient boosting (GB) (n = 4 studies), neural networks (NNs) (n = 3 studies), deep learning (n = 2 studies), and k-nearest neighbor (k-NN) (n = 2 studies), respectively. Forty-four features of inputs were used in the included studies, and age and gender are the most common features in the ML model. Discussion: There is no single algorithm that performed better or worse than all others at classifying patients with stroke, in part because different input data require different algorithms to achieve optimal outcomes.