Diagnosis of Schizophrenia: A comprehensive evaluation

TL;DR

This study evaluates various machine learning classification models and feature selection techniques on MRI data to improve the diagnosis of Schizophrenia, highlighting the importance of model and feature choice.

Contribution

It systematically compares multiple classifiers and feature selection methods, identifying the most effective combination for Schizophrenia diagnosis using MRI data.

Findings

SVM with Gaussian kernel outperformed other classifiers.

Wilcoxon feature selection was the most effective.

Combining grey and white matter data improved accuracy.

Abstract

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.