MRI feature engineering and SVM framework for schizophrenia recognition.

Purpose: Early diagnosis of schizophrenia plays a crucial role in improving patients' prognosis and effectively reducing the social burden. However, traditional diagnosis methods mainly rely on the subjectivity of clinical evaluation and lack objective quantitative basis, which poses significant challenges to the early recognition of schizophrenia. In recent years, although machine learning methods based on neuroimaging have made certain progress, when dealing with high dimensional, small sample MRI data, there are still problems such as low automation of feature extraction and insufficient model generalisation ability.

Methods: To address these issues, we propose MRI feature engineering and support vector machines (SVM) framework for schizophrenia recognition. First, the framework reduces the structural differences between individuals through preprocessing operations such as skull stripping and data registration. Second, it extracts macroscopic statistical features and optimises the feature set by screening key region-of-interest features using feature masking technology. Finally, it uses the SVM to analyse the discriminative patterns of features to complete the recognition.

Results: On the COBRE dataset, this paper uses five-fold cross-validation to comprehensively evaluate the model performance. The experimental results show that the average classification accuracy of this method reaches 95.00%. Meanwhile, it significantly outperforms six mainstream machine learning algorithms in multiple metrics.

Conclusions: This paper provides an objective and innovative approach for the auxiliary diagnosis of schizophrenia and offers strong support for its early intervention practices.