Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations
Open accessAbstract
BACKGROUND: A unique archive of Big Data on Parkinson's Disease is collected, managed and disseminated by the Parkinson's Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson's disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data-large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources-all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data. METHODS AND FINDINGS: Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson's disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson's Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting. CONCLUSIONS: Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson's disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer's, Huntington's, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications.
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Sample imbalances were often ignored, which results in biased models and misleading performance metrics.
“Sample imbalances were often ignored, which results in biased models and misleading performance metrics75,151.”
From Multimodal machine learning in precision health: A scoping review (2022) · cites this paper as [75]
The Methods (Data management; page unavailable) say the authors rebalanced patient and control groups to reduce potential bias and spurious effects from unequal sample sizes. The Discussion (page unavailable) reports substantially higher classification accuracy in rebalanced samples. These passages support the concern about bias and performance measures, but do not establish that sample imbalances were often ignored. (LLM, conf=0.86)
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Intelligent Big Data Analytics in Health
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Torres-Parga, A., Gershanik, O. S., Cardona, S., Guerrero, J., Gonzalez-Ojeda, L. M., & Cardona, J. F. (2025) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2025.108009 · Cited by 6 · see citing works
Embracing the promise of artificial intelligence to improve patient care in movement disorders
Bhidayasiri, R., & Goetz, C. G. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-99494-1.00015-0 · Cited by 6 · see citing works
Ghaderi, S., Mohammadi, M., Sayehmiri, F., Mohammadi, S., Tavasol, A., Rezaei, M., & Ghalyanchi‐Langeroudi, A. (2024) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29364 · Cited by 6 · see citing works
Early Prediction of Parkinson’s Disease using Multiple SVM Classifiers
Kumar, R., Gupta, M., & Singh, S. (2023) · International Conference on Sustainable Computing and Smart Systems (ICSCSS) · doi:10.1109/icscss57650.2023.10169162 · Cited by 6 · see citing works
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Big Data and Parkinson’s Disease: Exploration, Analyses, and Data Challenges.
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Time-Guided High-Order Attention Model of Longitudinal Heterogeneous Healthcare Data
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Big Data, Artificial Intelligence, and Machine Learning in Neurotrauma
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Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning Approach
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Desde las figlinae a los edificios: el uso del barro cocido en el sur de la Baetica
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A Study on Benefits of Big Data for Healthcare Sector of India
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Machine learning for the diagnosis of Parkinson's disease: A systematic review
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National Security: Theories, Actors, Issues
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- Authors
- Dinov, I. D., Heavner, B., Tang, M., Glusman, G., Chard, K., Darcy, M., Madduri, R., Pa, J., Spino, C., Kesselman, C., Foster, I., Deutsch, E. W., Price, N. D., Van Horn, J. D., Ames, J., Clark, K., Hood, L., Hampstead, B. M., Dauer, W., & Toga, A. W.
- Affiliations
- University of Southern California (US)
- Institute for Systems Biology (US)
- University of Michigan (US)
- Argonne National Laboratory (US)
- Corresponding author
- Dinov, Ivo D.
- ORCID
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-08-05
- Volume
- 11
- Issue
- 8
- Pages
- e0157077
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4975403
- OpenAlex
- W2409767563
- Cited by
- 145
- References
- 118
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 13 · 2023: 11 · 2022: 15 · 2021: 19 · 2020: 21 · 2019: 18 · 2018: 16 · 2017: 15 · 2016: 1
- FWCI
- 28.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, other-oa)
- Knowledge@UChicago (University of Chicago) (submittedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- University of Chicago (cc-by)
- Funders
- National Institutes of Health (P20 NR015331, U54 EB020406, P50 NS091856)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Neurology › Parkinson's Disease Mechanisms and Treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- Parkinson's disease, Parkinson's Progression Markers Initiative, big data analytics, multi-source data fusion, disease risk prediction, machine learning classification, neuroimaging biomarkers, UPDRS scores, neurodegeneration, data harmonization
- MeSH terms
- Aged, Female, Humans, Male, Parkinson Disease, Logistic Models, Databases, Factual, Disease Progression, Neuroimaging, Support Vector Machine
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