scholarly journals Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0239615
Author(s):  
Maeri Yamamoto ◽  
Epifanio Bagarinao ◽  
Itaru Kushima ◽  
Tsutomu Takahashi ◽  
Daiki Sasabayashi ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Magnetic Resonance ◽  
Support Vector Machine Classifier ◽  
Classification Performance ◽  
Support Vector ◽  
Resonance Imaging ◽  
Vector Machines ◽  
Structural Brain Alterations

Structural brain alterations have been repeatedly reported in schizophrenia; however, the pathophysiology of its alterations remains unclear. Multivariate pattern recognition analysis such as support vector machines can classify patients and healthy controls by detecting subtle and spatially distributed patterns of structural alterations. We aimed to use a support vector machine to distinguish patients with schizophrenia from control participants on the basis of structural magnetic resonance imaging data and delineate the patterns of structural alterations that significantly contributed to the classification performance. We used independent datasets from different sites with different magnetic resonance imaging scanners, protocols and clinical characteristics of the patient group to achieve a more accurate estimate of the classification performance of support vector machines. We developed a support vector machine classifier using the dataset from one site (101 participants) and evaluated the performance of the trained support vector machine using a dataset from the other site (97 participants) and vice versa. We assessed the performance of the trained support vector machines in each support vector machine classifier. Both support vector machine classifiers attained a classification accuracy of >70% with two independent datasets indicating a consistently high performance of support vector machines even when used to classify data from different sites, scanners and different acquisition protocols. The regions contributing to the classification accuracy included the bilateral medial frontal cortex, superior temporal cortex, insula, occipital cortex, cerebellum, and thalamus, which have been reported to be related to the pathogenesis of schizophrenia. These results indicated that the support vector machine could detect subtle structural brain alterations and might aid our understanding of the pathophysiology of these changes in schizophrenia, which could be one of the diagnostic findings of schizophrenia.

scholarly journals Support Vector Machine-Based Backprojection Algorithm for Detection of Gastric Cancer Lesions with Abdominal Endoscope Using Magnetic Resonance Imaging Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Huajia Dai ◽  
Yuhao Bian ◽  
Libin Wang ◽  
Junfeng Yang
Keyword(s):  
Magnetic Resonance Imaging ◽  
Gastric Cancer ◽  
Support Vector Machine ◽  
Magnetic Resonance ◽  
Hospital Stay ◽  
Radical Resection ◽  
Support Vector ◽  
Control Group ◽  
Resonance Imaging ◽  
Experimental Group

This study was to analyze the diagnostic value of magnetic resonance imaging (MRI) for gastric cancer (GC) lesions and the treatment effect of complete laparoscopic radical resection (CLSRR). A malignant tumor recognition algorithm was constructed in this study based on the backprojection (BP) and support vector machine (SVM), which was named BPS. 78 GC patients were divided into an experimental group (received CLSRR) and a control group (received assisted laparoscopic radical resection (ALSRR)), with 39 cases in each group. It was found that the BPS algorithm showed lower relative mean square error (MSE) in axle x (OMSE, x) and axle y (OMSE, x), but the classification accuracy (CA) was the opposite ( P < 0.05 ). The postoperative hospital stay, analgesia duration, first exhaust time (FET), and first off-bed activity time (FOBA) for patients in the experimental group were less ( P < 0.05 ). The operation time of the experimental group (270.56 ± 90.55 min) was significantly longer than that of the control group (228.07 ± 75.26 min) ( P < 0.05 ). There were 3 cases of anastomotic fistula, 1 case of acute peritonitis, and 2 cases of lung infections in the experimental group, which were greatly less than those in the control group (7 cases, 4 cases, and 3 cases) ( P < 0.05 ). In short, the BPS algorithm was superior in processing MRI images and could improve the diagnostic effect of MRI images. The CLSRR could reduce the length of hospital stay and the probability of complications in GC patients, so it could be used as a surgical plan for the clinical treatment of advanced GC.

scholarly journals Multilevel Clustering-Evolutionary Random Support Vector Machine Cluster Algorithm-Based Functional Magnetic Resonance Imaging in Diagnosing Cerebral Ischemic Stroke

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kun Fan ◽  
Ting Zhang ◽  
Weihong He
Keyword(s):  
Magnetic Resonance Imaging ◽  
Support Vector Machine ◽  
Magnetic Resonance ◽  
Cognitive Assessment ◽  
Intervention Group ◽  
Support Vector ◽  
Control Group ◽  
Resonance Imaging ◽  
Cerebral Ischemic Stroke ◽  
Cerebral Ischemic

This study was to explore the value of the blood oxygenation level dependent-functional magnetic resonance imaging (BOLD-fMRI) image classification based on the multilevel clustering-evolutionary random support vector machine cluster (MCRSVMC) algorithm in the diagnosis and treatment of patients with cognitive impairment after cerebral ischemic stroke (CIS). The MCRSVMC algorithm was optimized using a clustering algorithm, and it was compared with other algorithms in terms of accuracy (ACC), sensitivity (SEN), and specificity (SPE) of classifying the brain area images. 36 patients with cognitive impairment after CIS and nondementia patients were divided into a control group (drug treatment) and an intervention group (drug + acupuncture) according to different treatment methods, with 18 cases in each group. The changes in regional homogeneity (ReHo) of BOLD-fMRI images and the differences in scores of the Montreal Cognitive Assessment Scale (MoCA), scores of Loewenstein Occupational Therapy Cognitive Assessment (LOTCA), and scores of Functional Independence Measure (FIM) between the two groups of patients were compared before and after treatment. The results revealed that the average classification ACC, SEN, and SPE of the MCRSVMC algorithm were 84.25 ± 4.13%, 91.07 ± 3.51%, and 89 ± 3.96%, respectively, which were all obviously better than those of other algorithms ( P < 0.01 ). When the number of support vector machine (SVM) classifiers and the number of important features were 410 and 260, respectively, the classification ACC of MCRSVMC algorithm was 0.9429 and 0.9092, respectively. After treatment, the MoCA score, LOTCA score, and FIM score of the patients in the intervention group were higher than those of the control group ( P < 0.05 ). The ReHo values of the right inferior temporal gyrus and right inferior frontal gyrus of patients in the intervention group were much higher than those of the control group ( P < 0.05 ). It indicated that the classification ACC, SEN, and SPE of the magnetic resonance imaging (MRI) based on the MCRSVMC algorithm in this study were greatly improved, and the acupuncture method was more effective in the treatment of patients with cognitive dysfunction after CIS.

scholarly journals A support vector machine method for bid/no bid decision making

2017 ◽  
Vol 23 (5) ◽  
pp. 641-649 ◽  
Author(s):  
Rifat SONMEZ ◽  
Burak SÖZGEN
Keyword(s):  
Decision Making ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Support Vector Machine Classifier ◽  
Oil And Gas ◽  
Support Vector ◽  
Machine Method ◽  
Generalization Performance ◽  
Vector Machines ◽  
Input Variables

The bid/no bid decision is an important and complex process, and is impacted by numerous variables that are related to the contractor, project, client, competitors, tender and market conditions. Despite the complexity of bid decision making process, in the construction industry the majority of bid/no bid decisions is made informally based on experience, judgment, and perception. In this paper, a procedure based on support vector machines and backward elimination regression is presented for improving the existing bid decision making methods. The method takes advan­tage of the strong generalization properties of support vector machines and attempts to further enhance generalization performance by eliminating insignificant input variables. The method is implemented for bid/no bid decision making of offshore oil and gas platform fabrication projects to achieve a parsimonious support vector machine classifier. The performance of the support vector machine classifier is compared with the performances of the worth evaluation model, linear regression, and neural network classifiers. The results show that the support vector machine classifier outperforms existing methods significantly, and the proposed procedure provides a powerful tool for bid/no bid decision making. The results also reveal that elimination of the insignificant input variables improves generalization performance of the sup­port vector machines.

Sign in / Sign up

Export Citation Format

Share Document