Joint detection and clinical score prediction in Parkinson's disease via multi-modal sparse learning

In this study, a novel feature selection framework is proposed to simultaneously perform classification and clinical scores prediction of Parkinson's disease (PD) via multi-modal neuroimaging data. Specifically, a new feature selection model is devised to capture discriminative features to trai...

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Bibliographic Details
Published inProceedings (International Symposium on Biomedical Imaging) pp. 1231 - 1234
Main Authors Haijun Lei, Jian Zhang, Zhang Yang, Ee-leng Tan, Baiying Lei, Qiuming Luo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2017
Subjects
Classification
Diffusion tensor imaging
Diseases
Feature selection
Multi-modal data
Olfactory
Parkinson's disease
Prediction
Predictive models
Sleep
Support vector machines
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Summary:In this study, a novel feature selection framework is proposed to simultaneously perform classification and clinical scores prediction of Parkinson's disease (PD) via multi-modal neuroimaging data. Specifically, a new feature selection model is devised to capture discriminative features to train support vector regression model for clinical scores (e.g., sleep scores and olfactory scores) prediction and support vector classification model for class label identification. Our method is evaluated on a public dataset of 208 subjects including 56 normal controls (NC), 123 PD and 29 scans without evidence of dopamine deficit (SWEDD) via a 10-fold cross-validation method. The experimental results demonstrate that multi-modal data can effectively improve the performance in disease status identification and clinical scores prediction compared to one single modality. Our proposed method also outperforms the related methods.
ISSN:1945-8452
DOI:10.1109/ISBI.2017.7950739