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Song C, Zhao W, Jiang H, Liu X, Duan Y, Yu X, Yu X, Zhang J, Kui J, Liu C, Tang Y. Stability Evaluation of Brain Changes in Parkinson's Disease Based on Machine Learning. Front Comput Neurosci 2021; 15:735991. [PMID: 34795570 PMCID: PMC8594429 DOI: 10.3389/fncom.2021.735991] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 09/24/2021] [Indexed: 02/05/2023] Open
Abstract
Structural MRI (sMRI) has been widely used to examine the cerebral changes that occur in Parkinson's disease (PD). However, previous studies have aimed for brain changes at the group level rather than at the individual level. Additionally, previous studies have been inconsistent regarding the changes they identified. It is difficult to identify which brain regions are the true biomarkers of PD. To overcome these two issues, we employed four different feature selection methods [ReliefF, graph-theory, recursive feature elimination (RFE), and stability selection] to obtain a minimal set of relevant features and nonredundant features from gray matter (GM) and white matter (WM). Then, a support vector machine (SVM) was utilized to learn decision models from selected features. Based on machine learning technique, this study has not only extended group level statistical analysis with identifying group difference to individual level with predicting patients with PD from healthy controls (HCs), but also identified most informative brain regions with feature selection methods. Furthermore, we conducted horizontal and vertical analyses to investigate the stability of the identified brain regions. On the one hand, we compared the brain changes found by different feature selection methods and considered these brain regions found by feature selection methods commonly as the potential biomarkers related to PD. On the other hand, we compared these brain changes with previous findings reported by conventional statistical analysis to evaluate their stability. Our experiments have demonstrated that the proposed machine learning techniques achieve satisfactory and robust classification performance. The highest classification performance was 92.24% (specificity), 92.42% (sensitivity), 89.58% (accuracy), and 89.77% (AUC) for GM and 71.93% (specificity), 74.87% (sensitivity), 71.18% (accuracy), and 71.82% (AUC) for WM. Moreover, most brain regions identified by machine learning were consistent with previous findings, which means that these brain regions are related to the pathological brain changes characteristic of PD and can be regarded as potential biomarkers of PD. Besides, we also found the brain abnormality of superior frontal gyrus (dorsolateral, SFGdor) and lingual gyrus (LING), which have been confirmed in other studies of PD. This further demonstrates that machine learning models are beneficial for clinicians as a decision support system in diagnosing PD.
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Affiliation(s)
- Chenggang Song
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, Chengdu, China
- College of Computer, Chengdu University, Chengdu, China
| | - Weidong Zhao
- College of Computer, Chengdu University, Chengdu, China
| | - Hong Jiang
- Department of Neurosurgery, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaoju Liu
- Department of Abdominal Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yumei Duan
- Department of Computer and Software, Chengdu Jincheng College, Chengdu, China
| | - Xiaodong Yu
- College of Computer, Chengdu University, Chengdu, China
| | - Xi Yu
- College of Computer, Chengdu University, Chengdu, China
| | - Jian Zhang
- School of Physics and Electronic Engineering, Sichuan Normal University, Chengdu, China
| | - Jingyue Kui
- Department of Urology, Tonghai County People's Hospital, Yuxi, China
| | - Chang Liu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, Chengdu, China
- College of Computer, Chengdu University, Chengdu, China
| | - Yiqian Tang
- College of Computer, Chengdu University, Chengdu, China
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Ch S, Sohani S, Kumar D, Malik A, Chahar B, Nema A, Panigrahi B, Dhiman R. A Support Vector Machine-Firefly Algorithm based forecasting model to determine malaria transmission. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2013.09.030] [Citation(s) in RCA: 87] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Anguita D, Ghio A, Oneto L, Ridella S. In-sample and out-of-sample model selection and error estimation for support vector machines. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2012; 23:1390-406. [PMID: 24807923 DOI: 10.1109/tnnls.2012.2202401] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
In-sample approaches to model selection and error estimation of support vector machines (SVMs) are not as widespread as out-of-sample methods, where part of the data is removed from the training set for validation and testing purposes, mainly because their practical application is not straightforward and the latter provide, in many cases, satisfactory results. In this paper, we survey some recent and not-so-recent results of the data-dependent structural risk minimization framework and propose a proper reformulation of the SVM learning algorithm, so that the in-sample approach can be effectively applied. The experiments, performed both on simulated and real-world datasets, show that our in-sample approach can be favorably compared to out-of-sample methods, especially in cases where the latter ones provide questionable results. In particular, when the number of samples is small compared to their dimensionality, like in classification of microarray data, our proposal can outperform conventional out-of-sample approaches such as the cross validation, the leave-one-out, or the Bootstrap methods.
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