title: Predictive Big Data Analytics: A Study of Parkinson's Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations.
abstract: 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.
  
  
  RESULTS
  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.
authors: Statistics Online Computational Resource, School of Nursing, Michigan Institute for Data Science, University of Michigan, Ann Arbor, Michigan, United States of America.|Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, California, United States of America.|Udall Center of Excellence for Parkinson's Disease Research, University of Michigan, Ann Arbor, Michigan, United States of America.
authors: Ivo D
authors: ID
authors: Dinov
authors: Institute for Systems Biology, Seattle, Washington, United States of America.
authors: Ben
authors: B
authors: Heavner
authors: Statistics Online Computational Resource, School of Nursing, Michigan Institute for Data Science, University of Michigan, Ann Arbor, Michigan, United States of America.
authors: Ming
authors: M
authors: Tang
authors: Institute for Systems Biology, Seattle, Washington, United States of America.
authors: Gustavo
authors: G
authors: Glusman
authors: Computation Institute, University of Chicago and Argonne National Laboratory, Chicago, Illinois, United States of America.
authors: Kyle
authors: K
authors: Chard
authors: Information Sciences Institute, University of Southern California, Los Angeles, California, United States of America.
authors: Mike
authors: M
authors: Darcy
authors: Computation Institute, University of Chicago and Argonne National Laboratory, Chicago, Illinois, United States of America.
authors: Ravi
authors: R
authors: Madduri
authors: Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, California, United States of America.
authors: Judy
authors: J
authors: Pa
authors: Udall Center of Excellence for Parkinson's Disease Research, University of Michigan, Ann Arbor, Michigan, United States of America.
authors: Cathie
authors: C
authors: Spino
authors: Information Sciences Institute, University of Southern California, Los Angeles, California, United States of America.
authors: Carl
authors: C
authors: Kesselman
authors: Computation Institute, University of Chicago and Argonne National Laboratory, Chicago, Illinois, United States of America.
authors: Ian
authors: I
authors: Foster
authors: Institute for Systems Biology, Seattle, Washington, United States of America.
authors: Eric W
authors: EW
authors: Deutsch
authors: Institute for Systems Biology, Seattle, Washington, United States of America.
authors: Nathan D
authors: ND
authors: Price
authors: Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, California, United States of America.
authors: John D
authors: JD
authors: Van Horn
authors: Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, California, United States of America.
authors: Joseph
authors: J
authors: Ames
authors: Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, California, United States of America.
authors: Kristi
authors: K
authors: Clark
authors: Institute for Systems Biology, Seattle, Washington, United States of America.
authors: Leroy
authors: L
authors: Hood
authors: Department of Psychiatry and Michigan Alzheimer's Disease Center, University of Michigan, Ann Arbor, Michigan, United States of America.|Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan, United States of America.
authors: Benjamin M
authors: BM
authors: Hampstead
authors: Udall Center of Excellence for Parkinson's Disease Research, University of Michigan, Ann Arbor, Michigan, United States of America.
authors: William
authors: W
authors: Dauer
authors: Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, California, United States of America.
authors: Arthur W
authors: AW
authors: Toga
mesh_terms: D000368:Aged; D016208:Databases, Factual; D018450:Disease Progression; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D059906:Neuroimaging; D010300:Parkinson Disease / Q000175:diagnosis* / Q000235:genetics / Q000473:pathology; D060388:Support Vector Machine