As part of Alzheimer’s Disease Big Data DREAM Challenge #1 (AD#1), we want to quickly identify accurate predictive biomarkers of Alzheimer’s disease using an open scientific approach that can be used by scientific, industrial and regulatory communities to improve diagnosis and treatment. Using demographic, clinical and genetic data and MR imaging obtained from participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we created predictive models of cognitive assessments and predicted discrepancies between cognitive abilities and amyloid load. With data extraction, we compiled a useful subset of data from an enormous amount of it to use it as a training and test set. We have developed a system for fast and uniform processing and optimization of obtained data. This benefited us because we tried a variety of approaches and strategies to build a learning model for prediction and made a comparison of their effectiveness. We delved into much other research in this field, drew guidelines with them and compared the findings and results or efficiency with ours.
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