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

• Published in: Expert systems with applications Vol. 80; pp. 284 - 296
• Main Authors: Lei, Haijun, Huang, Zhongwei, Zhang, Jian, Yang, Zhang, Tan, Ee-Leng, Zhou, Feng, Lei, Baiying
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.09.2017; Elsevier BV
• Subjects: Artificial intelligence; Classification; Comparative analysis; Diagnosis; Dopamine; Feature selection; Machine learning; Mathematical models; Medical imaging; Mental depression; Modal data; Multi-modal data; Neurology; Parkinson's disease; Performance enhancement; Prediction; Predictions; Regression analysis; Rigidity; Sleep; Support vector machines; Multi-modal data; Feature selection; Parkinson's disease; Classification; Prediction

•A novel framework for joint PD detection and clinical score prediction is proposed.•A effective feature selection method for PD detection and prediction is proposed.•Multi-modal neuroimaging data enhances performance of PD detection. Parkinson's disease (PD) is the world's second most com...