  Machine learning models have been successfully employed in the diagnosis of
Schizophrenia disease. The impact of classification models and the feature
selection techniques on the diagnosis of Schizophrenia have not been evaluated.
Here, we sought to access the performance of classification models along with
different feature selection approaches on the structural magnetic resonance
imaging data. The data consist of 72 subjects with Schizophrenia and 74 healthy
control subjects. We evaluated different classification algorithms based on
support vector machine (SVM), random forest, kernel ridge regression and
randomized neural networks. Moreover, we evaluated T-Test, Receiver Operator
Characteristics (ROC), Wilcoxon, entropy, Bhattacharyya, Minimum Redundancy
Maximum Relevance (MRMR) and Neighbourhood Component Analysis (NCA) as the
feature selection techniques. Based on the evaluation, SVM based models with
Gaussian kernel proved better compared to other classification models and
Wilcoxon feature selection emerged as the best feature selection approach.
Moreover, in terms of data modality the performance on integration of the grey
matter and white matter proved better compared to the performance on the grey
and white matter individually. Our evaluation showed that classification
algorithms along with the feature selection approaches impact the diagnosis of
Schizophrenia disease. This indicates that proper selection of the features and
the classification models can improve the diagnosis of Schizophrenia.
