Cited by 244Parkinson's Disease Mechanisms and TreatmentsArtificial Intelligence in HealthcareDementia and Cognitive Impairment Research

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High-accuracy detection of early Parkinson’s disease through multimodal features and machine learning

Prashanth, R., Dutta Roy, Sumantra, Mandal, Pravat K., Ghosh, Shantanu·2016·International Journal of Medical Informatics

DOI 10.1016/j.ijmedinf.2016.03.001

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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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Background
Machine Learning Based Automatic Prediction of Parkinson’s Disease Using Speech Features
Jain, D., Mishra, A. K., & Das, S. K. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-4992-2_33 · Cited by 29
Detection of cognitive impairment using a machine-learning algorithm
Youn, Y. C., Choi, S. H., Shin, H., Kim, K. W., Jang, J., Jung, J. J., Hsiung, G. R., & Kim, S. (2018) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s171950 · Cited by 28
“27 However, the sensitivity of the algorithm using the KDSQ to detect the CI was 91.5%, which is good for a screening tool.”
Influential
A proof of concept for epidemiological research using structured reporting with pulmonary embolism as a use case
dos Santos, D. P., Scheibl, S., Arnhold, G., Mähringer‐Kunz, A., Düber, C., Mildenberger, P., & Kloeckner, R. (2018) · British Journal of Radiology · doi:10.1259/bjr.20170564 · Cited by 28
Artificial intelligence-based snakebite identification using snake images, snakebite wound images, and other modalities of information: A systematic review
Zhang, J., Chen, X., Song, A., & Li, X. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105024 · Cited by 27
A Methodological and Structural Review of Parkinson’s Disease Detection Across Diverse Data Modalities
Miah, A. S. M., Suzuki, T., & Shin, J. (2025) · IEEE Access · doi:10.1109/access.2025.3575023 · Cited by 25
Early and High‐Accuracy Diagnosis of Parkinson’s Disease: Outcomes of a New Model
Doumari, S. A., Berahmand, K., & Ebadi, M. J. (2023) · Computational and Mathematical Methods in Medicine · doi:10.1155/2023/1493676 · Cited by 28
Artificial Intelligence in Ophthalmology: Advantages and Limits
Costin, H. N., Fira, M., & Goraş, L. (2025) · Applied Sciences · doi:10.3390/app15041913 · Cited by 28
Decoding Intracranial EEG With Machine Learning: A Systematic Review
Mirchi, N., Warsi, N. M., Zhang, F., Wong, S. M., Suresh, H., Mithani, K., Erdman, L., & Ibrahim, G. M. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.913777 · Cited by 26
“This type of approach has also been applied for other applications relevant to neurology such as early detection of Parkinson’s disease (Prashanth et al., 2016), falls (Nahiduzzaman et al., 2020) and identiﬁcation of cortical dysplasia (Mo et al., 2019).”
Method
PDD-ET: Parkinson’s Disease Detection Using ML Ensemble Techniques and Customized Big Dataset
Chatterjee, K., Kumar, R. P., Bandyopadhyay, A., Swain, S., Mallik, S., Li, A., & Ray, K. (2023) · Information · doi:10.3390/info14090502 · Cited by 24
Advanced Parkinson’s Disease Detection: A comprehensive artificial intelligence approach utilizing clinical assessment and neuroimaging samples
Islam, N., Turza, M. S. A., Fahim, S. I., & Rahman, R. M. (2024) · International Journal of Cognitive Computing in Engineering · doi:10.1016/j.ijcce.2024.05.001 · Cited by 22
Explainable AI for Parkinson’s disease prediction: A machine learning approach with interpretable models
Esan, A. O., Olawade, D. B., Soladoye, A. A., Omodunbi, B. A., Adeyanju, I., & Aderinto, N. (2025) · Current Research in Translational Medicine · doi:10.1016/j.retram.2025.103541 · Cited by 27
PARNet: Deep neural network for the diagnosis of parkinson's disease
Keleş, A., Keles, M. B., Keles, M. B., & Okatan, A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16940-3 · Cited by 21
“Support vector machine (SVM) [9], decision tree, and linear regression classifiers are used in these studies where a person mainly extracts the features [10–12].”
A decision support system for selecting the most suitable machine learning in healthcare using user parameters and requirements
Ketkar, Y., & Gawade, S. D. (2022) · Healthcare Analytics · doi:10.1016/j.health.2022.100117 · Cited by 20
Multimodal feature learning and fusion on B-mode ultrasonography and sonoelastography using point-wise gated deep networks for prostate cancer diagnosis
Zhang, Q., Xiong, J., Cai, Y., Shi, J., Xu, S., & Zhang, Q. (2019) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt-2018-0136 · Cited by 21
“Prashanth et al. [13] showed that a combination of cerebrospinal fluid, non-motor and imaging markers may aid in the diagnosis of preclinical Parkinson’s disease.”
Background
Enhancing clinical trial outcome prediction with artificial intelligence: a systematic review
Qian, L., Lu, X., Haris, P. I., Zhu, J., Li, S., & Yang, Y. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104332 · Cited by 23
Predictive analytics and machine learning for medical informatics: A survey of tasks and techniques
Lamba, D., Hsu, W. H., & Alsadhan, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821777-1.00023-9 · Cited by 26
“(2016), who discuss experimental Parkinson’s treatments [242]; Kolachalama and Garg (2018) survey machine learning as a subarea of artificial intelligence (AI), particularly in medical education [243]; and BannachBrown et al.”
Background
Machine learning Ensemble for the Parkinson’s disease using protein sequences
Arora, P., Mishra, A., & Malhi, A. K. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12960-7 · Cited by 19
“Chen et al. [9] proposed a method using fuzzy k-nearest neighbour (FKNN) to diagnose PD and comparison has been conducted between SVM and FKNN. Prashanth et al. [38] applied SVM, Naive Bayes, Random Forests and Boosted Trees classifiers for early diagnosis of PD.”
Method
Recent Developments in Circulating Biomarkers in Parkinson’s Disease: The Potential Use of Mirnas in A Clinical Setting
dos Santos, M. C. T., Bell, R., & da Costa, A. N. (2016) · Bioanalysis · doi:10.4155/bio-2016-0166 · Cited by 19
Artificial intelligence techniques support nuclear medicine modalities to improve the diagnosis of Parkinson’s disease and Parkinsonian syndromes
Palumbo, B., Bianconi, F., Nuvoli, S. M. F., Spanu, A., & Fravolini, M. L. (2020) · Clinical and Translational Imaging · doi:10.1007/s40336-020-00404-x · Cited by 20
Early Diagnosis of Parkinson's Disease by Analyzing Magnetic Resonance Imaging Brain Scans and Patient Characteristic
Zhu, S. (2022) · International Conference on Bioinformatics and Computational Biology (ICBCB) · doi:10.1109/icbcb55259.2022.9802132 · Cited by 17
“Furthermore, some studies have used only patient scores or symptom-based assessments (Prashanth et al. 2016), along with other biomarkers.”
Decoding kinetic features of hand motor preparation from single‐trial EEG using convolutional neural networks
Gatti, R., Atum, Y. V., Schiaffino, L., Jochumsen, M., & Manresa, J. A. B. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.14936 · Cited by 18
Parkinson’s disease detection based on dysphonia measurements
Lahmiri, S. (2016) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2016.12.009 · Cited by 16
Applications of Machine Learning to Diagnosis of Parkinson’s Disease
Lai, H., Li, X., Xu, F., Zhu, J., Li, X., Yang, S. T., Wang, X., Wang, Z., & Wang, C. (2023) · Brain Sciences · doi:10.3390/brainsci13111546 · Cited by 15
“…Prashanth et al. utilized a combination of NMS features, cerebrospinal ﬂuid (CSF), and imaging markers to differentiate early PD subjects from normal individuals using the naïve Bayes, SVM, boosted trees, and RF methods, observing that the SVM classiﬁer delivered the most optimal performance [38].”
BackgroundMethod
A deep 1-D CNN learning approach with data augmentation for classification of Parkinson’s disease and scans without evidence of dopamine deficit (SWEDD)
Aggarwal, N., Saini, B. S., & Gupta, S. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106008 · Cited by 15
Artificial intelligence applied to neuroimaging data in Parkinsonian syndromes: Actuality and expectations
Vitale, A., Villa, R., Ugga, L., Romeo, V., Stanzione, A., & Cuocolo, R. (2021) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2021091 · Cited by 16
“Using PPMI dataset, Prashanth et al. [34] tried to find the best ML approach to identify early PD patients through the use of multimodal features such as non-motor features of RBD and olfactory loss, along with CSF measurements and dopaminergic imaging markers.”
Distribution of non-motor symptoms in idiopathic Parkinson’s disease and secondary Parkinsonism
Skogar, Ö., & Nilsson, M. (2018) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s170307 · Cited by 14
“31 However, these are not available in everyday practice and the clinicians often have to rely on their clinical skills to deal with the diagnostic challenges.”
Predictive modelling of Parkinson’s disease progression based on RNA-Sequence with densely connected deep recurrent neural networks
Ahmed, S., Komeili, M., & Park, J. (2022) · Scientific Reports · doi:10.1038/s41598-022-25454-1 · Cited by 13
Explainable AI approach for early detection of Parkinson’s disease using PPMI Online data
Aggarwal, N., Saxena, G. J., Singh, S., & Pundir, A. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10127-z · Cited by 16
Functional connectivity differences of the olfactory network in Parkinson’s Disease, mild cognitive impairment and cognitively normal individuals: A resting-state fMRI study
Cieri, F., Giriprakash, P. P., Nandy, R. R., Zhuang, X., Doty, R. L., Caldwell, J., & Cordes, D. (2024) · Neuroscience · doi:10.1016/j.neuroscience.2024.08.031 · Cited by 13
“These include preclinical diagnosis of PD using multimodal features including olfaction, cerebrospinal fluid (CSF), and imaging markers (Prashanth et al., 2016); identifying a specific pattern within the University of Pennsylvania Smell Identification Test (UPSIT) score (Doty & Kamath, 2014) that…”
A Smart Wearable Ring Device for Sensing Hand Tremor of Parkinson’s Patients
Yang, H., Shen, Y., Zhuang, W., Gao, C., Dai, D., & Zhang, W. (2021) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2021.014558 · Cited by 13
Cloud‐assisted Parkinson disease identification system for remote patient monitoring and diagnosis in the smart healthcare applications
Harimoorthy, K., & Menakadevi, T. (2021) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6419 · Cited by 13
Improved Automatic Morphology-Based Classification of Parkinson’s Disease and Progressive Supranuclear Palsy
Talai, A. S., Ismail, Z., Sedlacik, J., Boelmans, K., & Forkert, N. D. (2018) · Clinical Neuroradiology · doi:10.1007/s00062-018-0727-8 · Cited by 13
Long Short-Term Memory-Based Deep Learning Models for Screening Parkinson’s Disease Using Sequential Diagnostic Codes
Yoon, S., Kim, M., & Lee, W. (2023) · Journal of Clinical Neurology · doi:10.3988/jcn.2022.0160 · Cited by 15
“…tree and medium Gaussian SVM Gait impairments and tremor occurrences X Zeng et al. (2016) 14 Radial basis function neural networks Gait patterns X Prashanth et al. (2016) 15 SVM, naïve Bayes, boosted trees, and RF Nonmotor symptoms and biomarkers (PPMI) X Wang et al. (2020) 16 Three deep…”
Non-motor Clinical and Biomarker Predictors Enable High Cross-Validated Accuracy Detection of Early PD but Lesser Cross-Validated Accuracy Detection of Scans Without Evidence of Dopaminergic Deficit
Leger, C. S., Herbert, M., & DeSouza, J. F. X. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.00364 · Cited by 12
“Hyposmia and RBD are not specific to PD, yet hyposmia in particular has been the predictor of greatest import in early PD predictive modeling (43), the dominant predictive model driver for those with genetic risk (41), and of secondary import only to imaging in other early PD predictive modeling research (42).”
Background
Towards Automated Classification of Parkinson's Disease: Comparison of Machine Learning Methods using MRI and Acoustic Data
Shaffi, N., Viswan, V., Mahmud, M., Hajamohideen, F., & Subramanian, K. (2023) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci52147.2023.10371926 · Cited by 11
“Prasanth et al. [15] used RBD alongside other biomarkers, such as cerebrospinal fluid measurements and dopaminergic image markers, to distinguish PD from normal subjects.”
Background
Developing a Model for Parkinson’s Disease Detection Using Machine Learning Algorithms
Mudawi, N. A. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.048967 · Cited by 11
“Research by Prashantha et al. [13] distinguishes early PD patients from healthy controls.”
Skip-AttSeqNet: Leveraging skip connection and attention-driven Seq2seq model to enhance eye movement event detection in Parkinson’s disease
Wang, X., Fan, L., Li, H., Bi, X., Jiang, W., & Ma, X. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106862 · Cited by 11
Blood SSR1: A Possible Biomarker for Early Prediction of Parkinson’s Disease
Zhang, W., Shen, J., Wang, Y., Cai, K., Zhang, Q., & Cao, M. (2022) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2022.762544 · Cited by 11
REM sleep behavior disorder and cerebrospinal fluid alpha-synuclein, amyloid beta, total tau and phosphorylated tau in Parkinson’s disease: a cross-sectional and longitudinal study
Nabizadeh, F., Pirahesh, K., & Valizadeh, P. (2022) · Journal of Neurology · doi:10.1007/s00415-022-11120-z · Cited by 11

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.
ORCID: Prashanth, R.
Mandal, Pravat K.
Venue: International Journal of Medical Informatics
Publisher: Elsevier BV
Published: 2016-06
Volume: 90
Pages: 13-21
ISSN: 1386-5056 (ISSN-L)
DOI: 10.1016/j.ijmedinf.2016.03.001
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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High-accuracy detection of early Parkinson’s disease through multimodal features and machine learning

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