Identification of male schizophrenia patients using brain morphology based on machine learning algorithms

Abstract

BACKGROUND

Schizophrenia is a severe psychiatric disease, and its prevalence is higher. However, diagnosis of early-stage schizophrenia is still considered a challenging task.

AIM

To employ brain morphological features and machine learning method to differentiate male individuals with schizophrenia from healthy controls.

METHODS

The least absolute shrinkage and selection operator and t tests were applied to select important features from structural magnetic resonance images as input features for classification. Four commonly used machine learning algorithms, the general linear model, random forest (RF), k-nearest neighbors, and support vector machine algorithms, were used to develop the classification models. The performance of the classification models was evaluated according to the area under the receiver operating characteristic curve (AUC).

RESULTS

A total of 8 important features with significant differences between groups were considered as input features for the establishment of classification models based on the four machine learning algorithms. Compared to other machine learning algorithms, RF yielded better performance in the discrimination of male schizophrenic individuals from healthy controls, with an AUC of 0.886.

CONCLUSION

Our research suggests that brain morphological features can be used to improve the early diagnosis of schizophrenia in male patients.

Keywords: Schizophrenia; Machine learning; Classification; Structure; Magnetic Resonance imaging

Core Tip: Schizophrenia is a severe psychiatric disease characterized by impairments in cognition, positive and negative symptoms, affecting about 1% of the general population worldwide. A fast diagnosis of schizophrenia is crucial to prescription of an appropriate anti-psychotic in the early stage, which is able to make treatment more efficient. Many studies have demonstrated widespread functional and structural brain alternations from magnetic resonance imaging in individuals with schizophrenia in relation to healthy controls. our aims were to employ four commonly used machine learning algorithms including general linear model, random forest, k-nearest neighbors, and support vector machine and a wider range of brain morphological features to avoid bias towards a particular machine learning algorithm and improve the performance of classification between male individuals with schizophrenia and healthy controls in the present study.