Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images

Unipolar depression (UD), also referred to as clinical depression, appears to be a widespread mental disorder around the world. Further, this is a vital state related to a person’s health that influences his/her daily routine. Besides, this state also influences the person’s frame of mind, behavior, and several body functionalities like sleep, appetite, and also it can cause a scenario where a person could harm himself/herself or others. In several cases, it becomes an arduous task to detect UD, since, it is a state of comorbidity. For that reason, this research proposes a more convenient approach for the physicians to detect the state of clinical depression at an initial phase using an integrated multistage support vector machine model. Initially, the dataset is preprocessed using multiple imputation by chained equations (MICE) technique. Then, for selecting the appropriate features, the support vector machine-based recursive feature elimination (SVM RFE) is deployed. Subsequently, the integrated multistage support vector machine classifier is built by employing the bagging random sampling technique. Finally, the experimental outcomes indicate that the proposed integrated multistage support vector machine model surpasses methods such as logistic regression, multilayer perceptron, random forest, and bagging SVM (majority voting), in terms of overall performance.

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The potential of support vector machine as the diagnostic tool for schizophrenia: A systematic literature review of neuroimaging studies

European Psychiatry ◽

10.1016/s0924-9338(11)73068-1 ◽

2011 ◽

Vol 26 (S2) ◽

pp. 1363-1363 ◽

Cited By ~ 4

Author(s):

M.P. Collins ◽

S.E. Pape

Keyword(s):

Support Vector Machine ◽

Diagnostic Tool ◽

Literature Search ◽

Structural Mri ◽

Support Vector ◽

Systematic Literature Search ◽

Resonance Imaging ◽

Pubmed Database ◽

Magnetic Resonance Imaging Mri ◽

The Individual

IntroductionSchizophrenia is a relatively common chronic psychotic mental illness, which usually continues throughout life. Current diagnosis is based on a set of psychiatrist-applied diagnostic criteria. There can be considerable differences between diagnostic classification based upon either the set of criteria used, or the individual who applies the criteria. For this reason, the development of an objective test to inform the diagnosis could be highly beneficial.ObjectivesTo assess the use of Support Vector Machine (SVM) as a potential diagnostic tool for schizophrenia, with a particular focus on the application of SVM to Magnetic Resonance Imaging (MRI) data.AimsTo show the use of SVM on MRI data to be a potentially viable diagnostic test.MethodA systematic literature search was carried out using the PubMed database, Web of Knowledge as well as Google Scholar. This search was conducted using the terms ‘Schizophrenia’, ‘SVM’/‘Support Vector Machine’ and ‘MRI/fMRI’. This was followed by the application of criteria relating to relevance to the desired search topic (as assesed by the author). Ten publications were identified as relevant.ResultsResults showed strong evidence that the application of SVM to MRI data can reliably differentiate between patients with schizophrenia and healthy controls.ConclusionsThe results indicate that using SVM to analyse MRI data can be reliably used to identify schizophrenia, although there is some variability between the results produced. The potential of SVM in application to fMRI (as opposed to structural MRI) data is yet to be fully explored.

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Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine

NeuroImage ◽

10.1016/j.neuroimage.2016.02.044 ◽

2016 ◽

Vol 132 ◽

pp. 157-166 ◽

Cited By ~ 13

Author(s):

Kristin A. Linn ◽

Bilwaj Gaonkar ◽

Theodore D. Satterthwaite ◽

Jimit Doshi ◽

Christos Davatzikos ◽

...

Keyword(s):

Support Vector Machine ◽

Pattern Analysis ◽

Structural Mri ◽

Support Vector ◽

Control Group ◽

Multivariate Pattern Analysis ◽

Feature Normalization ◽

Multivariate Pattern

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Support Vector Machine - Recursive Feature Elimination (SVM - RFE) for Selection of MicroRNA Expression Features of Breast Cancer

2018 2nd International Conference on Informatics and Computational Sciences (ICICoS) ◽

10.1109/icicos.2018.8621708 ◽

2018 ◽

Author(s):

Amazona Adorada ◽

Ratih Permatasari ◽

Panji Wisnu Wirawan ◽

Adi Wibowo ◽

Adi Sujiwo

Keyword(s):

Breast Cancer ◽

Support Vector Machine ◽

Microrna Expression ◽

Recursive Feature Elimination ◽

Support Vector ◽

Selection Of

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Hybrid adapted fast correlation FCBF-support vector machine recursive feature elimination for feature selection

Intelligent Decision Technologies ◽

10.3233/idt-190014 ◽

2020 ◽

Vol 14 (3) ◽

pp. 269-279

Author(s):

Hayet Djellali ◽

Nacira Ghoualmi-Zine ◽

Souad Guessoum

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Recursive Feature Elimination ◽

Support Vector ◽

Svm Classifier ◽

Hybrid Architecture ◽

Features Selection ◽

K Nearest Neighbors ◽

Correlation Based Feature Selection ◽

Embedded Method

This paper investigates feature selection methods based on hybrid architecture using feature selection algorithm called Adapted Fast Correlation Based Feature selection and Support Vector Machine Recursive Feature Elimination (AFCBF-SVMRFE). The AFCBF-SVMRFE has three stages and composed of SVMRFE embedded method with Correlation based Features Selection. The first stage is the relevance analysis, the second one is a redundancy analysis, and the third stage is a performance evaluation and features restoration stage. Experiments show that the proposed method tested on different classifiers: Support Vector Machine SVM and K nearest neighbors KNN provide a best accuracy on various dataset. The SVM classifier outperforms KNN classifier on these data. The AFCBF-SVMRFE outperforms FCBF multivariate filter, SVMRFE, Particle swarm optimization PSO and Artificial bees colony ABC.

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ITERATIVE FEATURE PERTURBATION AS A GENE SELECTOR FOR MICROARRAY DATA

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001412600038 ◽

2012 ◽

Vol 26 (05) ◽

pp. 1260003 ◽

Cited By ~ 16

Author(s):

JUANA CANUL-REICH ◽

LAWRENCE O. HALL ◽

DMITRY B. GOLDGOF ◽

JOHN N. KORECKI ◽

STEVEN ESCHRICH

Keyword(s):

Gene Expression ◽

Colon Cancer ◽

Support Vector Machine ◽

Performance Improvement ◽

Microarray Data ◽

T Test ◽

Recursive Feature Elimination ◽

Support Vector ◽

Gene Sets ◽

Microarray Datasets

Gene-expression microarray datasets often consist of a limited number of samples with a large number of gene-expression measurements, usually on the order of thousands. Therefore, dimensionality reduction is critical prior to any classification task. In this work, the iterative feature perturbation method (IFP), an embedded gene selector, is introduced and applied to four microarray cancer datasets: colon cancer, leukemia, Moffitt colon cancer, and lung cancer. We compare results obtained by IFP to those of support vector machine-recursive feature elimination (SVM-RFE) and the t-test as a feature filter using a linear support vector machine as the base classifier. Analysis of the intersection of gene sets selected by the three methods across the four datasets was done. Additional experiments included an initial pre-selection of the top 200 genes based on their p values. IFP and SVM-RFE were then applied on the reduced feature sets. These results showed up to 3.32% average performance improvement for IFP across the four datasets. A statistical analysis (using the Friedman/Holm test) for both scenarios showed the highest accuracies came from the t-test as a filter on experiments without gene pre-selection. IFP and SVM-RFE had greater classification accuracy after gene pre-selection. Analysis showed the t-test is a good gene selector for microarray data. IFP and SVM-RFE showed performance improvement on a reduced by t-test dataset. The IFP approach resulted in comparable or superior average class accuracy when compared to SVM-RFE on three of the four datasets. The same or similar accuracies can be obtained with different sets of genes.

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