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Machine Learning Based Modeling of Cognitive Performance from Life-style Data

Jazayeri, Farnaz | 2021

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 54674 (02)
  4. University: Sharif University of Technology
  5. Department: Mathematical Sciences
  6. Advisor(s): Razvan, Mohammad Reza; Khaligh Razavi, Mahdi
  7. Abstract:
  8. For neurodegenerative diseases like Multiple Sclerosis, Alzheimer’s, or Parkinson’s disease early detection is required to slow progression and prevent disease onset. To do so, identifying early signs and symptoms of the disease as well as modifying lifestyle can play a crucial role. Nowadays, the increasing use of smart gadgets and sensors has paved the way for collecting behavioral data and therefore analyzing and extracting meaningful patterns. In this study, lifestyle and cognitive performance data have been collected via a platform called OptiMind. Previous studies have shown that the Integrated Cognitive Assessment (ICA) can identify patients with neurodegenerative disorders (such as Alzheimer’s disease) from healthy ones. Moreover, ICA has no learning bias, which means users can frequently monitor their cognitive performance by using it. Additionally, in comparison with other tests, users’ level of education has less impact on their cognitive score. The main goal of this study is to investigate the impact of lifestyle factors (such as sleep, physical activity, heart rate, etc) on the cognitive score. After necessary preprocessing and feature extraction, we utilize machine learning methods to model users’ cognitive scores based on their lifestyles. Afterward, by interpreting computational models we inform users how their daily behavior can affect their cognitive performance
  9. Keywords:
  10. Data Analysis ; Machine Learning ; Interpretability ; Lifestyle ; Early Detection ; Cognitive Assessment

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