Evaluating the performance of machine learning models for automatic diagnosis of patients with schizophrenia based on a single site dataset of 440 participants

Evaluating the performance of machine learning models for automatic diagnosis of patients with schizophrenia based on a single site dataset of 440 participants

Abstract Background Support vector machines (SVMs) based on brain-wise functional connectivity (FC) have been widely adopted for single-subject prediction of patients with schizophrenia, but most of them had small sample size. This study aimed to evaluate the performance of SVMs based on a large s...

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Bibliographic Details
Main Authors: Lung-Hao Lee, Chang-Hao Chen, Wan-Chen Chang, Po-Lei Lee, Kuo-Kai Shyu, Mu-Hong Chen, Ju-Wei Hsu, Ya-Mei Bai, Tung-Ping Su, Pei-Chi Tu
Format: Article
Language:English
Published: Cambridge University Press 2022-01-01
Series:European Psychiatry
Subjects:
Automatic classification
functional connectivity
homogeneous
schizophrenic disorder
support vector machine
training sample size
Online Access:https://www.cambridge.org/core/product/identifier/S0924933821022483/type/journal_article

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