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Int J Med Inform. 2016 Jun;90:13-21. doi: 10.1016/j.ijmedinf.2016.03.001. Epub 2016 Mar 5.

High-Accuracy Detection of Early Parkinson's Disease through Multimodal Features and Machine Learning.

Author information

1
Department of Electrical Engineering, Indian Institute of Technology, Delhi, India. Electronic address: prashanth.r.itd@gmail.com.
2
Department of Electrical Engineering, Indian Institute of Technology, Delhi, India.
3
Neuroimaging and Neurospectroscopy Laboratory, National Brain Research Centre, India; Department of Radiology, Johns Hopkins Medicine, MD, USA.
4
Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, MA, USA.

Abstract

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 Naïve 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.

KEYWORDS:

Computer-aided diagnosis; Parkinson’s disease; cerebrospinal fluid markers; dopaminergic imaging; non-motor features; pattern classification

PMID:
27103193
DOI:
10.1016/j.ijmedinf.2016.03.001
[Indexed for MEDLINE]

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