ArticleComputational and mathematical methods in medicine2022
Improved Multiclassification of Schizophrenia Based on Xgboost and Information Fusion for Small Datasets.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- An innovative framework based on GMV-ALFF multi-modal MRI and Gaussian data augmentation for psychiatric disorder classification.Frontiers in computational neuroscience · 2026Article
- Maximum and minimum activity in inpatient adolescents with Bipolar Disorders: Daily-Variability classification of actigraphy pattern with artificial intelligence.Psychiatry research communications · 2025Article
- Diagnosis of mental disorders using machine learning: Literature review and bibliometric mapping from 2012 to 2023.Heliyon · 2024Review
- Advancing EGFR mutation subtypes prediction in NSCLC by combining 3D pretrained ConvNeXt, radiomics, and clinical features.Frontiers in oncology · 2024Article
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3 authors.
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Abstract
To improve the performance in multiclass classification for small datasets, a new approach for schizophrenic classification is proposed in the present study. Firstly, the Xgboost classifier is introduced to discriminate the two subtypes of schizophrenia from health controls by analyzing the functional magnetic resonance imaging (fMRI) data, while the gray matter volume (GMV) and amplitude of low-frequency fluctuations (ALFF) are extracted as the features of classifiers. Then, the D-S combination rule of evidence is used to achieve fusion to determine the basic probability assignment based on the output of different classifiers. Finally, the algorithm is applied to classify 38 healthy controls, 16 deficit schizophrenic patients, and 31 nondeficit schizophrenic patients. 10-folds cross-validation method is used to assess classification performance. The results show the proposed algorithm with a sensitivity of 73.89%, which is higher than other classification algorithms, such as supported vector machine (SVM), logistic regression (LR),
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