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Detection of Schizophrenia Cases From Healthy Controls With Combination of Neurocognitive and Electrophysiological Features

BACKGROUND: The search for a method that utilizes biomarkers to identify patients with schizophrenia from healthy individuals has occupied researchers for decades. However, no single indicator can be employed to achieve the good in clinical practice. We aim to develop a comprehensive machine learnin...

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Autores principales: Tian, Qing, Yang, Ning-Bo, Fan, Yu, Dong, Fang, Bo, Qi-Jing, Zhou, Fu-Chun, Zhang, Ji-Cong, Li, Liang, Yin, Guang-Zhong, Wang, Chuan-Yue, Fan, Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9016153/
https://www.ncbi.nlm.nih.gov/pubmed/35449564
http://dx.doi.org/10.3389/fpsyt.2022.810362
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author Tian, Qing
Yang, Ning-Bo
Fan, Yu
Dong, Fang
Bo, Qi-Jing
Zhou, Fu-Chun
Zhang, Ji-Cong
Li, Liang
Yin, Guang-Zhong
Wang, Chuan-Yue
Fan, Ming
author_facet Tian, Qing
Yang, Ning-Bo
Fan, Yu
Dong, Fang
Bo, Qi-Jing
Zhou, Fu-Chun
Zhang, Ji-Cong
Li, Liang
Yin, Guang-Zhong
Wang, Chuan-Yue
Fan, Ming
author_sort Tian, Qing
collection PubMed
description BACKGROUND: The search for a method that utilizes biomarkers to identify patients with schizophrenia from healthy individuals has occupied researchers for decades. However, no single indicator can be employed to achieve the good in clinical practice. We aim to develop a comprehensive machine learning pipeline based on neurocognitive and electrophysiological combined features for distinguishing schizophrenia patients from healthy people. METHODS: In the present study, 69 patients with schizophrenia and 50 healthy controls participated. Neurocognitive (contains seven specific domains of cognition) and electrophysiological [prepulse inhibition, electroencephalography (EEG) power spectrum, detrended fluctuation analysis, and fractal dimension (FD)] features were collected, all these features were taken together to generate the identification models of schizophrenia by applying logistics, random forest, and extreme gradient boosting algorithm. The classification capabilities of these models were also evaluated. RESULTS: Both the neurocognitive and electrophysiological feature sets showed a good classification effect with the highest accuracy greater than 85% and AUC greater than 90%. Specifically, the performances of the combined neurocognitive and electrophysiological feature sets achieved the highest accuracy of 93.28% and AUC of 97.91%. The extreme gradient boosting algorithm as a whole presented more stably and precisely in classification efficiency. CONCLUSION: The highest classification accuracy of 93.28% by combination of neurocognitive and electrophysiological features shows that both measurements are appropriate indicators to be used in discriminating schizophrenia patients and healthy individuals. Also, among three algorithms, extreme gradient boosting had better classified performances than logistics and random forest algorithms.
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spelling pubmed-90161532022-04-20 Detection of Schizophrenia Cases From Healthy Controls With Combination of Neurocognitive and Electrophysiological Features Tian, Qing Yang, Ning-Bo Fan, Yu Dong, Fang Bo, Qi-Jing Zhou, Fu-Chun Zhang, Ji-Cong Li, Liang Yin, Guang-Zhong Wang, Chuan-Yue Fan, Ming Front Psychiatry Psychiatry BACKGROUND: The search for a method that utilizes biomarkers to identify patients with schizophrenia from healthy individuals has occupied researchers for decades. However, no single indicator can be employed to achieve the good in clinical practice. We aim to develop a comprehensive machine learning pipeline based on neurocognitive and electrophysiological combined features for distinguishing schizophrenia patients from healthy people. METHODS: In the present study, 69 patients with schizophrenia and 50 healthy controls participated. Neurocognitive (contains seven specific domains of cognition) and electrophysiological [prepulse inhibition, electroencephalography (EEG) power spectrum, detrended fluctuation analysis, and fractal dimension (FD)] features were collected, all these features were taken together to generate the identification models of schizophrenia by applying logistics, random forest, and extreme gradient boosting algorithm. The classification capabilities of these models were also evaluated. RESULTS: Both the neurocognitive and electrophysiological feature sets showed a good classification effect with the highest accuracy greater than 85% and AUC greater than 90%. Specifically, the performances of the combined neurocognitive and electrophysiological feature sets achieved the highest accuracy of 93.28% and AUC of 97.91%. The extreme gradient boosting algorithm as a whole presented more stably and precisely in classification efficiency. CONCLUSION: The highest classification accuracy of 93.28% by combination of neurocognitive and electrophysiological features shows that both measurements are appropriate indicators to be used in discriminating schizophrenia patients and healthy individuals. Also, among three algorithms, extreme gradient boosting had better classified performances than logistics and random forest algorithms. Frontiers Media S.A. 2022-04-05 /pmc/articles/PMC9016153/ /pubmed/35449564 http://dx.doi.org/10.3389/fpsyt.2022.810362 Text en Copyright © 2022 Tian, Yang, Fan, Dong, Bo, Zhou, Zhang, Li, Yin, Wang and Fan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychiatry
Tian, Qing
Yang, Ning-Bo
Fan, Yu
Dong, Fang
Bo, Qi-Jing
Zhou, Fu-Chun
Zhang, Ji-Cong
Li, Liang
Yin, Guang-Zhong
Wang, Chuan-Yue
Fan, Ming
Detection of Schizophrenia Cases From Healthy Controls With Combination of Neurocognitive and Electrophysiological Features
title Detection of Schizophrenia Cases From Healthy Controls With Combination of Neurocognitive and Electrophysiological Features
title_full Detection of Schizophrenia Cases From Healthy Controls With Combination of Neurocognitive and Electrophysiological Features
title_fullStr Detection of Schizophrenia Cases From Healthy Controls With Combination of Neurocognitive and Electrophysiological Features
title_full_unstemmed Detection of Schizophrenia Cases From Healthy Controls With Combination of Neurocognitive and Electrophysiological Features
title_short Detection of Schizophrenia Cases From Healthy Controls With Combination of Neurocognitive and Electrophysiological Features
title_sort detection of schizophrenia cases from healthy controls with combination of neurocognitive and electrophysiological features
topic Psychiatry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9016153/
https://www.ncbi.nlm.nih.gov/pubmed/35449564
http://dx.doi.org/10.3389/fpsyt.2022.810362
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