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.