Date of Award

6-10-2021

Document Type

Thesis

Publisher

Santa Clara : Santa Clara University, 2021.

Department

Computer Science and Engineering

First Advisor

Ahmed Amer

Abstract

Alzheimer’s Disease is the 6th leading cause of death overall and the most common cause of dementia in older people in the US. The prevalence of the disease is projected to increase in the next few decades and disproportionately impact low/middle income populations. Unfortunately, specialized doctors, such as neurologists, may not be present in situations where a diagnosis is necessary, resulting in the possibility of AD being overlooked at its early and most treatable stages. Our proposed application is a tool that can aid doctors in determining a probable AD diagnosis using an inputted combination of imaging data, biomarkers, patient medical history data, and cognitive and functional assessments into a random forest machine learning model. It has a user interface designed with a focus on accessibility and simplicity. Our trained decision tree achieves an accuracy of 90% for a binary classification between CN and AD patients, and an accuracy of 77% for a multi-class classification between CN, MCI, and AD patients.

Download

Share

COinS