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Parkinson's Disease - OSTI GOV

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Last Updated: 27 April 2022

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Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations

Progression Markers Initiative, a Parkinson's Progression Markers Initiative assembles and disseminates a unique archive of Big Data on Parkinson's Disease, as well as the Parkinson's Progression Markers Initiative. Many previous human and animal studies have looked at the connection between Parkinson's disease risk to trauma, genetics, environment, co-morbidities, or lifestyle. Big Data's key features include: large size, incongruency, incompleteness, extensicity, multiplicity of scales, and heterogeneity of information-generation sources, all pose challenges to the classical methods for data processing, visualization, and interpretation. We jointly processed complicated PPMI imaging, genetics, clinical, and demographic data in order to determine PD risk using Big Data technology. Methods and Results A collective representation of multi-source data aids in the aggregation and harmonization of complex data elements. We created a complete guide for end-to-end data analysis, manipulation, processing, cleaning, analysis, and validation using heterogeneous PPMI results. Specifically, we develop tools for rebalancing imbalanced cohorts, employ a variety of classification technologies to produce uniform and reliable phenotypic forecasts, and establish a reproducible machine-learning framework that allows the reporting of model parameters and diagnostic forecasting based on new data. Conclusions Model-based Big Data machine learning-based classification methods can outperform model-based methods in terms of predictive accuracy and reliability. We found that statistical rebalancing of cohort sizes yields improved classification of group differences, particularly for predictive analytics based on heterogeneous and incomplete PPMI results. Also without longitudinal UPDRS results, however, the certainty of model-free machine learning based classification is over 80%.

Source link: https://www.osti.gov/biblio/1627792

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions