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Principal Investigator  
Principal Investigator's Name: Jennifer Barreto
Institution: Medical Academy for Science and Technology
Department: Science Dept
Proposed Analysis: With this data, we plan to identify patterns and the mechanisms by which Alzheimer's disease develops and how it can be more accurately predicted based on various parameters. We will be using shallow machine learning algorithms (such as Support Vector Machine and Random Forests) as well as deep learning models to try to identify the biomarkers associated with the progression of AD and predict AD in patients based on various variables, including MRI data, PET scan data, and RNA sequencing data. We will be comparing these algorithms to one another in terms of accuracy, sensitivity, and overall performance. The results of this study will serve to show the most valuable biomarkers that can be used when diagnosing AD. It will also shine a light on the effectiveness of using multiple models based on different types of data to predict a specific disease, or whether any one parameter (i.e., genetic data, MRI data, PET data, etc.) proves most effective.
Additional Investigators