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High-accuracy detection of early Parkinson’s disease through multimodal features and machine learning
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.
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“Prashanth et al. (2016) demonstrated even higher precision (accuracy: 96.4%, sensitivity: 97.03%) by SVM, which integrates nonmotor features (including sleep behaviour disorders and olfactory loss), CSF biomarkers, and PET imaging data”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Prashanth et al., 2016
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BackgroundMethodResultInfluentialClinical applications of deep learning in neurology and its enhancements with future predictions
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Metadata
- Authors
- Prashanth, R., Dutta Roy, S., Mandal, P. K., & Ghosh, S.
- Affiliations
- Indian Institute of Technology Delhi (IN)
- Johns Hopkins University (US)
- Harvard University (US)
- Corresponding author
- Prashanth, R.
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2016-06
- Volume
- 90
- Pages
- 13-21
- ISSN
- 1386-5056 (ISSN-L)
- OpenAlex
- W2290432223
- Cited by
- 244
- References
- 39
- Citations by year
- 2026: 28 · 2025: 40 · 2024: 31 · 2023: 36 · 2022: 37 · 2021: 21 · 2020: 20 · 2019: 10 · 2018: 12 · 2017: 6 · 2016: 3
- FWCI
- 7.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Medical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Michael J
- Topics
- Health Sciences › Medicine › Clinical Neurology › Parkinson's Disease Mechanisms and Treatments
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- Parkinson's disease, early diagnosis, preclinical diagnosis, REM sleep behavior disorder, olfactory loss, non-motor symptoms, cerebrospinal fluid biomarkers, multimodal fusion, Parkinson's Progression Markers Initiative, support vector machine, classifier comparison
- MeSH terms
- Machine Learning, Olfaction Disorders, Humans, Parkinson Disease, Models, Statistical, Tomography, Emission-Computed, Single-Photon, Databases, Factual, REM Sleep Behavior Disorder, Early Diagnosis
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