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Machine learning algorithm performance evaluation in structural magnetic resonance imaging-based classification of pediatric bipolar disorders type I patients
The diagnosis based on clinical assessment of pediatric bipolar disorder (PBD) may sometimes lead to misdiagnosis in clinical practice. For the past several years, machine learning (ML) methods were introduced for the classification of bipolar disorder (BD), which were helpful in the diagnosis of BD...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9445985/ https://www.ncbi.nlm.nih.gov/pubmed/36082304 http://dx.doi.org/10.3389/fncom.2022.915477 |
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author | Dou, Ruhai Gao, Weijia Meng, Qingmin Zhang, Xiaotong Cao, Weifang Kuang, Liangfeng Niu, Jinpeng Guo, Yongxin Cui, Dong Jiao, Qing Qiu, Jianfeng Su, Linyan Lu, Guangming |
author_facet | Dou, Ruhai Gao, Weijia Meng, Qingmin Zhang, Xiaotong Cao, Weifang Kuang, Liangfeng Niu, Jinpeng Guo, Yongxin Cui, Dong Jiao, Qing Qiu, Jianfeng Su, Linyan Lu, Guangming |
author_sort | Dou, Ruhai |
collection | PubMed |
description | The diagnosis based on clinical assessment of pediatric bipolar disorder (PBD) may sometimes lead to misdiagnosis in clinical practice. For the past several years, machine learning (ML) methods were introduced for the classification of bipolar disorder (BD), which were helpful in the diagnosis of BD. In this study, brain cortical thickness and subcortical volume of 33 PBD-I patients and 19 age-sex matched healthy controls (HCs) were extracted from the magnetic resonance imaging (MRI) data and set as features for classification. The dimensionality reduced feature subset, which was filtered by Lasso or f_classif, was sent to the six classifiers (logistic regression (LR), support vector machine (SVM), random forest classifier, naïve Bayes, k-nearest neighbor, and AdaBoost algorithm), and the classifiers were trained and tested. Among all the classifiers, the top two classifiers with the highest accuracy were LR (84.19%) and SVM (82.80%). Feature selection was performed in the six algorithms to obtain the most important variables including the right middle temporal gyrus and bilateral pallidum, which is consistent with structural and functional anomalous changes in these brain regions in PBD patients. These findings take the computer-aided diagnosis of BD a step forward. |
format | Online Article Text |
id | pubmed-9445985 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94459852022-09-07 Machine learning algorithm performance evaluation in structural magnetic resonance imaging-based classification of pediatric bipolar disorders type I patients Dou, Ruhai Gao, Weijia Meng, Qingmin Zhang, Xiaotong Cao, Weifang Kuang, Liangfeng Niu, Jinpeng Guo, Yongxin Cui, Dong Jiao, Qing Qiu, Jianfeng Su, Linyan Lu, Guangming Front Comput Neurosci Computational Neuroscience The diagnosis based on clinical assessment of pediatric bipolar disorder (PBD) may sometimes lead to misdiagnosis in clinical practice. For the past several years, machine learning (ML) methods were introduced for the classification of bipolar disorder (BD), which were helpful in the diagnosis of BD. In this study, brain cortical thickness and subcortical volume of 33 PBD-I patients and 19 age-sex matched healthy controls (HCs) were extracted from the magnetic resonance imaging (MRI) data and set as features for classification. The dimensionality reduced feature subset, which was filtered by Lasso or f_classif, was sent to the six classifiers (logistic regression (LR), support vector machine (SVM), random forest classifier, naïve Bayes, k-nearest neighbor, and AdaBoost algorithm), and the classifiers were trained and tested. Among all the classifiers, the top two classifiers with the highest accuracy were LR (84.19%) and SVM (82.80%). Feature selection was performed in the six algorithms to obtain the most important variables including the right middle temporal gyrus and bilateral pallidum, which is consistent with structural and functional anomalous changes in these brain regions in PBD patients. These findings take the computer-aided diagnosis of BD a step forward. Frontiers Media S.A. 2022-08-23 /pmc/articles/PMC9445985/ /pubmed/36082304 http://dx.doi.org/10.3389/fncom.2022.915477 Text en Copyright © 2022 Dou, Gao, Meng, Zhang, Cao, Kuang, Niu, Guo, Cui, Jiao, Qiu, Su and Lu. 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 | Computational Neuroscience Dou, Ruhai Gao, Weijia Meng, Qingmin Zhang, Xiaotong Cao, Weifang Kuang, Liangfeng Niu, Jinpeng Guo, Yongxin Cui, Dong Jiao, Qing Qiu, Jianfeng Su, Linyan Lu, Guangming Machine learning algorithm performance evaluation in structural magnetic resonance imaging-based classification of pediatric bipolar disorders type I patients |
title | Machine learning algorithm performance evaluation in structural magnetic resonance imaging-based classification of pediatric bipolar disorders type I patients |
title_full | Machine learning algorithm performance evaluation in structural magnetic resonance imaging-based classification of pediatric bipolar disorders type I patients |
title_fullStr | Machine learning algorithm performance evaluation in structural magnetic resonance imaging-based classification of pediatric bipolar disorders type I patients |
title_full_unstemmed | Machine learning algorithm performance evaluation in structural magnetic resonance imaging-based classification of pediatric bipolar disorders type I patients |
title_short | Machine learning algorithm performance evaluation in structural magnetic resonance imaging-based classification of pediatric bipolar disorders type I patients |
title_sort | machine learning algorithm performance evaluation in structural magnetic resonance imaging-based classification of pediatric bipolar disorders type i patients |
topic | Computational Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9445985/ https://www.ncbi.nlm.nih.gov/pubmed/36082304 http://dx.doi.org/10.3389/fncom.2022.915477 |
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