The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
No free lunch theorems for optimization
Wolpert, D. H., & Macready, W. G. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585893 · Cited by 14,237
Parkinsonism: onset, progression, and mortality
Hoehn, M. M., & Yahr, M. D. (1967) · Neurology · doi:10.1212/wnl.17.5.427 · Cited by 12,107
A Short Introduction to Boosting
Freund, Y., & Schapire, R. E. (1999) · Cited by 2,991
Uneven Pattern of Dopamine Loss in the Striatum of Patients with Idiopathic Parkinson's Disease
Kish, S. J., Shannak, K. S., & Hornykiewicz, O. (1988) · New England Journal of Medicine · doi:10.1056/nejm198804073181402 · Cited by 1,945
The Parkinson Progression Marker Initiative (PPMI)
MAREK, K. L., Jennings, D. L., Lasch, S., Siderowf, A. D., Tanner, C. M., Simuni, T., Coffey, C. S., Kieburtz, K. D., Flagg, E., Chowdhury, S., et al. (2011) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2011.09.005 · Cited by 1,934
(2001) · Cited by 1,932
Doty, R. L., Shaman, P., & Dann, M. (1984) · Physiology & Behavior · doi:10.1016/0031-9384(84)90269-5 · Cited by 1,854
A brief introduction to boosting
Schapire, R. E. (1999) · Cited by 1,301
The REM sleep behavior disorder screening questionnaire—A new diagnostic instrument
Stiasny‐Kolster, K., Mayer, G., Schäfer, S., Möller, J. C., Heinzel‐Gutenbrunner, M., & Oertel, W. H. (2007) · Movement Disorders · doi:10.1002/mds.21740 · Cited by 1,065
Description of Parkinson's Disease as a Clinical Syndrome
Fahn, S. (2003) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2003.tb07458.x · Cited by 979
Iranzo, Á., Molinuevo, J. L., Santamaría, J., Serradell, M., Martı́, M. J., Valldeoriola, F., & Tolosa, E. S. (2006) · The Lancet Neurology · doi:10.1016/s1474-4422(06)70476-8 · Cited by 976
Steyerberg, E. W., Eijkemans, M. J. C., Harrell, F. E., & Habbema, J. D. F. (2000) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(20000430)19:8 · Cited by 789
Quantifying the risk of neurodegenerative disease in idiopathic REM sleep behavior disorder
Postuma, R. B., Gagnon, J., Vendette, M., Fantini, M. L., Massicotte-Marquez, J., & Montplaisir, J. Y. (2008) · Neurology · doi:10.1212/01.wnl.0000340980.19702.6e · Cited by 779
Idiopathic hyposmia as a preclinical sign of Parkinson's disease
Ponsen, M. M., Stoffers, D., Booij, J., van Eck‐Smit, B. L. F., Wolters, E. C., & Berendse, H. W. (2004) · Annals of Neurology · doi:10.1002/ana.20160 · Cited by 758
Advances in Neural Information Processing Systems 15 (NIPS 2002)
Williams, C. K. I., & Shawe-Taylor, J. S. (2002) · Cited by 612
Multiple comparison analysis testing in ANOVA
McHugh, M. L. (2011) · Biochemia Medica · doi:10.11613/bm.2011.029 · Cited by 532
Olsson, A., Vanderstichele, H., Andreasen, N., Meyer, G. D., Wallin, A., Holmberg, B., Rosengren, L., Vanmechelen, E., & Blennow, K. (2004) · Clinical Chemistry · doi:10.1373/clinchem.2004.039347 · Cited by 431
Stiasny‐Kolster, K. (2004) · Brain · doi:10.1093/brain/awh322 · Cited by 430
Cerebrospinal fluid biomarkers for Parkinson disease diagnosis and progression
Shi, M. Y., Bradner, J. M., Hancock, A. M., Chung, K. A., Quinn, J. F., Peskind, E. R., Galasko, D., Jankovic, J. J., Zabetian, C. P., Kim, H. M., et al. (2010) · Annals of Neurology · doi:10.1002/ana.22311 · Cited by 422
Kang, J. (2013) · JAMA Neurology · doi:10.1001/jamaneurol.2013.3861 · Cited by 366
Piggott, M. A., Marshall, E. F., Thomas, N., Lloyd, S. R., COURT, J. A., Jaros, E., Burn, D. J., Johnson, M. K., Perry, R. H., McKeith, I. G., et al. (1999) · Brain · doi:10.1093/brain/122.8.1449 · Cited by 327
Berendse, H. W., Booij, J., Francot, C. M. J. E., Bergmans, P., Hijman, R., Stoof, J. C., & Wolters, E. C. (2001) · Annals of Neurology · doi:10.1002/ana.1049 · Cited by 298
Biomarkers in Parkinson’s disease (recent update)
Sharma, S. K., Moon, C. S., Khogali, A., Haidous, A., Chabenne, A., Ojo, C., Jelebinkov, M., Kurdi, Y., & Ebadi, M. (2013) · Neurochemistry International · doi:10.1016/j.neuint.2013.06.005 · Cited by 249
Nomura, T., Inoue, Y., Kagimura, T., Uemura, Y., & Nakashima, K. (2011) · Sleep Medicine · doi:10.1016/j.sleep.2011.01.015 · Cited by 233
The role of dopaminergic imaging in patients with symptoms of dopaminergic system neurodegeneration
Cummings, J. L., Henchcliffe, C., Schaier, S., Simuni, T., Waxman, A., & Kemp, P. M. (2011) · Brain · doi:10.1093/brain/awr177 · Cited by 218
Prashanth, R., Roy, S. D., Mandal, P. K., & Ghosh, S. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.11.031 · Cited by 140
Self-citationThe use of smell identification tests in the diagnosis of Parkinson's disease in Brazil
Silveira‐Moriyama, L., Carvalho, M. D. J., Katzenschlager, R., Petrie, A., Ranvaud, R., Barbosa, E. R., & Lees, A. J. (2008) · Movement Disorders · doi:10.1002/mds.22241 · Cited by 106
Automatic assistance to Parkinsonˈs disease diagnosis in DaTSCAN SPECT imaging
Illán, I. A., Górriz, J. M., Ramı́rez, J., Segovia, F., Jiménez-Hoyuela, J. M., & Lozano, S. J. O. (2012) · Medical Physics · doi:10.1118/1.4742055 · Cited by 97
Classification of speech intelligibility in Parkinson's disease
Khan, T., Westin, J., & Dougherty, M. S. (2013) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2013.10.003 · Cited by 84
Improved Parkinsonism diagnosis using a partial least squares based approach
Segovia, F., Górriz, J. M., Ramı́rez, J., Álvarez, I., Jiménez-Hoyuela, J. M., & Ortega, S. J. (2012) · Medical Physics · doi:10.1118/1.4730289 · Cited by 67
Unveiling relevant non-motor Parkinson's disease severity symptoms using a machine learning approach
Armañanzas, R., Bielza, C., Chaudhuri, К. R., Martínez‐Martín, P., & Larrañaga, P. (2013) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2013.04.002 · Cited by 65
Berendse, H. W., & Ponsen, M. M. (2009) · Parkinsonism & Related Disorders · doi:10.1016/s1353-8020(09)70774-6 · Cited by 60
Accurate telemonitoring of Parkinson's disease diagnosis using robust inference system
Mandal, I., & Sairam, N. (2012) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2012.10.006 · Cited by 51
Automatic classification of 123I-FP-CIT (DaTSCAN) SPECT images
Towey, D., Bain, P. G., & Nijran, K. S. (2011) · Nuclear Medicine Communications · doi:10.1097/mnm.0b013e328347cd09 · Cited by 43
Technology Insight: imaging neurodegeneration in Parkinson's disease
Brooks, D. J. (2008) · Nature Clinical Practice Neurology · doi:10.1038/ncpneuro0773 · Cited by 43
The use of a color coded probability scale to interpret smell tests in suspected parkinsonism
Silveira‐Moriyama, L., Petrie, A., Williams, D. R., Evans, A. H., Katzenschlager, R., Barbosa, E. R., & Lees, A. J. (2009) · Movement Disorders · doi:10.1002/mds.22494 · Cited by 37
Cited by
Sorted by citations
Multimodal machine learning in precision health: A scoping review
Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., & Luo, Y. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00712-8 · Cited by 528
“Of those, most were published using medical imaging and EHR data 34,36,48,53,56,57,60,62 – 64, 68, 71, 73, 75, 85 – 88, 90, 92 – 96 , 98, 100, 104, the time series information using t-SNE plots 58 .”
Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature
Mei, J., Desrosiers, C., & Frasnelli, J. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.633752 · Cited by 441
“…method of 8 models (SVM, MLP, logistic regression, random forest, NSVC, decision tree, KNN, QDA) Sensitivity = 96% Specificity = 97% AUC = 0.98 2017 Adams, 2017 Classification of PD, HC and other neurological stance disorders Diagnosis and differential diagnosis Collected from participants 293; 57…”
MethodInfluentialEarly Detection of Parkinson’s Disease Using Deep Learning and Machine Learning
Wang, W., Lee, J., Harrou, F., & Sun, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3016062 · Cited by 423
“To guarantee early detection of PD, the premotor or prodromal stage in PD should be carefully monitored [24], [25].”
BackgroundMethodIntegrated multimodal artificial intelligence framework for healthcare applications
Soenksen, L. R., Ma, Y., Zeng, C., Boussioux, L., Villalobos Carballo, K., Na, L., Wiberg, H. M., Li, M. L., Fuentes, I., & Bertsimas, D. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00689-4 · Cited by 380
“…domains of cardiology 7 – 9 , dermatology 10 , gastroenterology 11 , gynecology 12 , hematology 13 , immunology 14 , nephrology 15 , neurology 16,17 , oncology 18 – 20 , ophthalmology 21 , psychiatry 22 , radiology 23 – 25 , public health 26 and healthcare operational analytics (i.e.,…”
The role of artificial neural network and machine learning in utilizing spatial information
Goel, A., Kumar, A., & Kumar, A. (2022) · Spatial Information Research · doi:10.1007/s41324-022-00494-x · Cited by 304
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
