Yu T, Pei WZ, Xu CY, Deng CC, Zhang XL. Identification of male schizophrenia patients using brain morphology based on machine learning algorithms. World J Psychiatry 2024; 14(6): 804-811 [PMID: 38984327 DOI: 10.5498/wjp.v14.i6.804]
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Psychiatry. Jun 19, 2024; 14(6): 804-811 Published online Jun 19, 2024. doi: 10.5498/wjp.v14.i6.804
Identification of male schizophrenia patients using brain morphology based on machine learning algorithms
Tao Yu, Wen-Zhi Pei, Chun-Yuan Xu, Chen-Chen Deng, Xu-Lai Zhang
Tao Yu, Chun-Yuan Xu, Department of Clinical Nutrition, Hefei Fourth People’s Hospital, Hefei 230032, Anhui Province, China
Wen-Zhi Pei, Xu-Lai Zhang, Department of Psychiatry, Hefei Fourth People’s Hospital, Hefei 230032, Anhui Province, China
Chen-Chen Deng, Department of Gynaecology, Anhui Maternal and Child Health Hospital, Hefei 230032, Anhui Province, China
Author contributions: Yu T designed the study, analyzed the data, and wrote the manuscript; Pei WZ collected the relevant data; Zhang XL provided financial support; Xu CY provided technological support; Deng CC edited the manuscript; and all authors have read and approved the final manuscript.
Supported bythe University Research Fund of Anhui Medical University, No. 2022xkj119.
Institutional review board statement: The study was reviewed and approved by the Ethics Committee of the Fourth People’s Hospital of Hefei [Approval No. HFSY-IRB-YJ-KYXM-YT (2024-003-001)].
Informed consent statement: All participants enrolled into this study provided informed written consent prior to study enrollment.
Conflict-of-interest statement: The authors declare no conflict of interest.
Data sharing statement: Data used in this study can be available from the corresponding author at 479800330@qq.com.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Received: March 15, 2024 Revised: May 1, 2024 Accepted: May 21, 2024 Published online: June 19, 2024 Processing time: 96 Days and 10.2 Hours
Abstract
BACKGROUND
Schizophrenia is a severe psychiatric disease, and its prevalence is higher. However, diagnosis of early-stage schizophrenia is still considered a challenging task.
AIM
To employ brain morphological features and machine learning method to differentiate male individuals with schizophrenia from healthy controls.
METHODS
The least absolute shrinkage and selection operator and t tests were applied to select important features from structural magnetic resonance images as input features for classification. Four commonly used machine learning algorithms, the general linear model, random forest (RF), k-nearest neighbors, and support vector machine algorithms, were used to develop the classification models. The performance of the classification models was evaluated according to the area under the receiver operating characteristic curve (AUC).
RESULTS
A total of 8 important features with significant differences between groups were considered as input features for the establishment of classification models based on the four machine learning algorithms. Compared to other machine learning algorithms, RF yielded better performance in the discrimination of male schizophrenic individuals from healthy controls, with an AUC of 0.886.
CONCLUSION
Our research suggests that brain morphological features can be used to improve the early diagnosis of schizophrenia in male patients.
Core Tip: Schizophrenia is a severe psychiatric disease characterized by impairments in cognition, positive and negative symptoms, affecting about 1% of the general population worldwide. A fast diagnosis of schizophrenia is crucial to prescription of an appropriate anti-psychotic in the early stage, which is able to make treatment more efficient. Many studies have demonstrated widespread functional and structural brain alternations from magnetic resonance imaging in individuals with schizophrenia in relation to healthy controls. our aims were to employ four commonly used machine learning algorithms including general linear model, random forest, k-nearest neighbors, and support vector machine and a wider range of brain morphological features to avoid bias towards a particular machine learning algorithm and improve the performance of classification between male individuals with schizophrenia and healthy controls in the present study.
