Determining Cost-Efficient Indicators of Alzheimer’s Disease Using Neural Networking

Overview

Mililani High School Comparison of methods used to diagnose Alzheimer’s Disease (AD) via imaging, mental, and genetic data remains an open area of investigation. In this investigation, it was hypothesized that after designing and creating a full feed-forward neural network that accurately classifies cognitively normal (CN), mildly cognitively impaired (MCI), or Alzheimer’s Disease (AD) patients, the top indicators of AD will include ApoE ε4 and structural volumetric measures. A key public AD Neuroimaging Initiative dataset containing 68 potential indicators for 14,037 patients was cleaned, one-hot encoded, and normalized to 25 potential key indicators and 2,646 patients. A full feed - forward network was carefully designed, which employed a categorical cross-entropy loss function and Adam Stochastic Gradient Descent Optimizer, to determine the most accurate indicators of AD. After more than 1,000 trials were run, Principal Component Analysis and BigML Analysis were conducted to cross-verify the results of the neural network. A cost analysis was also conducted to assess the practicality and accessibility of these top indicators. The results partially supported the hypothesis, suggesting that ApoE ε4, Clinical Dementia Ratin g Scale Sum of Boxes, the 11 and 13 item versions of ADAS-Cog, ADASQ4, the Mini-Mental State Examination, hippocampal volumetric measurements, and intracranial volume adjustment measurements most accurately indicate AD. Based on the cost analysis, getting a Mini-Mental State Examination ($79.35) and ApoE ε4 testing ($99-$199) are the most cost-efficient and accurate indicators of AD.

Competition history

• JSHS 2020 Category not listed

Resources

• National JSHS abstract book ↗

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