Weighted Deep Forest for Schizophrenia Data Classification

Abstract

There is no objective biological indicator for the diagnosis of schizophrenia. Machine learning is used to classify functional magnetic resonance imaging (fMRI) data, the aim of which is to effectively improve the reliability of diagnostics for schizophrenia. The following points are often considered: 1) Extracting effective features from fMRI data. 2) Choosing an appropriate machine learning method. 3) Improving classification accuracy. In this paper, we propose a weighted deep forest model, which includes a weighted class vector, and a prediction class vector. In our experiment, we extract functional connection (FC) features from fMRI data. Then, we use principal component analysis (PCA) to reduce the dimension of FC features. For datasets with unbalanced data, we use SMOTE to balance the data. Finally, the datasets with balanced data are fed into the weighted forest model. Compared with the classification results obtained by traditional classifiers, our classification accuracy is better. This method will provide greater possibilities for assisting doctors in diagnosing schizophrenia. This paper has significance for the study of schizophrenia by helping doctors diagnose the disease.