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An Ensemble Hybrid Feature Selection Method for Neuropsychiatric Disorder Classification.

Liangliang Liu, Shaojie Tang, Fang-Xiang Wu

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
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    This study introduces a novel hybrid feature selection method combining 3D DenseNet and XGBoost for improved classification of neuropsychiatric disorders using MRI and phenotypic data.

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    Area of Science:

    • Neuroscience
    • Computer Science
    • Medical Imaging

    Background:

    • Magnetic resonance imaging (MRI) offers valuable data for studying neuropsychiatric disorders.
    • Limitations exist in distinguishing disease subclasses using single data types.

    Purpose of the Study:

    • To propose an ensemble hybrid feature selection method for enhanced neuropsychiatric disorder classification.
    • To integrate structural MRI image features with phenotypic data for a comprehensive analysis.

    Main Methods:

    • Utilized a 3D DenseNet for selecting image features from structural MRI.
    • Employed XGBoost for selecting phenotypic features from records.
    • Combined image and phenotypic features to create a hybrid feature set.

    Main Results:

    • The hybrid feature approach significantly improved classification performance.
    • Achieved a best accuracy of 91.22% for binary and 78.62% for multi-class classification.
    • Demonstrated the importance of integrating phenotypic and MRI image features.

    Conclusions:

    • The proposed hybrid feature selection method enhances the accuracy of neuropsychiatric disorder classification.
    • Structural MRI data, when combined with phenotypic information, is crucial for distinguishing disorders.
    • Feature visualization provided insights into brain region involvement in different disorders.