Suebsarn RuksakulpiwatWitchuda ThongkingWendie ZhouChitchanok BenjasirisanLalipat PhianhasinNicholas K. SchiltzSmit BrahmbhattHarbin Medical UniversityMahidol UniversityShibaura Institute of TechnologyCase Western Reserve University2022-08-042022-08-042021-01-01Chronic Illness. (2021)17459206174239532-s2.0-85121364309https://repository.li.mahidol.ac.th/handle/20.500.14594/78546Objective: 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.Mahidol UniversityMedicineReviewSCOPUS10.1177/17423953211067435