期刊
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
卷 90, 期 -, 页码 13-21出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.ijmedinf.2016.03.001
关键词
Parkinson's disease; non-motor features; cerebrospinal fluid markers; dopaminergic imaging; Computer-aided diagnosis; pattern classification
资金
- Michael J. Fox Foundation for Parkinson's Research
- Abbott Laboratories
- Avid Radiopharmaceuticals
- Biogen Idec
- Bristol-Myers Squibb
- Covance
- Elan
- GE Healthcare
- Genentech
- GlaxoSmithKline
- Eli Lilly and Company
- Merck Co.
- Meso Scale Discovery
- Pfizer
- Hoffmann-La Roche
- UCB (Union Chimique Beige)
- Piramal
Early (or preclinical) diagnosis of Parkinson's disease (PD) is crucial for its early management as by the time manifestation of clinical symptoms occur, more than 60% of the dopaminergic neurons have already been lost. It is now established that there exists a premotor stage, before the start of these classic motor symptoms, characterized by a constellation of clinical features, mostly non-motor in nature such as Rapid Eye Movement (REM) sleep Behaviour Disorder (RBD) and olfactory loss. In this paper, we use the non motor features of RBD and olfactory loss, along with other significant biomarkers such as Cerebrospinal fluid (CSF) measurements and dopaminergic imaging markers from 183 healthy normal and 401 early PD subjects, as obtained from the Parkinson's Progression Markers Initiative (PPMI) database, to classify early PD subjects from normal using Naive Bayes, Support Vector Machine (SVM), Boosted Trees and Random Forests classifiers. We observe that SVM classifier gave the best performance (96.40% accuracy, 97.03% sensitivity, 95.01% specificity, and 98.88% area under ROC). We infer from the study that a combination of non-motor, CSF and imaging markers may aid in the preclinical diagnosis of PD. (C) 2016 Elsevier Ireland Ltd. All rights reserved.
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