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구분 SCI
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학술지 Prediction of Poststroke Depression Based on the Outcomes of Machine Learning Algorithms
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저자
류영환, 김서영, 김태욱, 이성재, 박수준, 정호열, 현정근
발행일
202204
출처
Journal of Clinical Medicine, v.11 no.8, pp.1-21
ISSN
2077-0383
출판사
MDPI
DOI
https://dx.doi.org/10.3390/jcm11082264
초록
Poststroke depression (PSD) is a major psychiatric disorder that develops after stroke; however, whether PSD treatment improves cognitive and functional impairments is not clearly understood. We reviewed data from 31 subjects with PSD and 34 age-matched controls without PSD; all subjects underwent neurological, cognitive, and functional assessments, including the National Institutes of Health Stroke Scale (NIHSS), the Korean version of the Mini-Mental Status Examination (K-MMSE), computerized neurocognitive test (CNT), the Korean version of the Modified Barthel Index (K-MBI), and functional independence measure (FIM) at admission to the rehabilitation unit in the subacute stage following stroke and 4 weeks after initial assessments. Machine learning methods, such as support vector machine, k-nearest neighbors, random forest, voting ensemble models, and statistical analysis using logistic regression were performed. PSD was successfully predicted using a support vector machine with a radial basis function kernel function (area under curve (AUC) = 0.711, accuracy = 0.700). PSD prognoses could be predicted using a support vector machine linear algorithm (AUC = 0.830, accuracy = 0.771). The statistical method did not have a better AUC than that of machine learning algorithms. We concluded that the occurrence and prognosis of PSD in stroke patients can be predicted effectively based on patients?? cognitive and functional statuses using machine learning algorithms.
KSP 제안 키워드
Area Under Curve, Ensemble models, Functional independence, Kernel function, Linear Algorithm, Logistic Regression(LR), Machine Learning Algorithms, Machine Learning Methods, Poststroke depression(PSD), Psychiatric disorders, Radial Basis Function Kernel
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