Krishna Iyer

Identifying biomarkers in different demographics of Alzheimer patients

Started Mar. 4, 2024

Abstract or project description

The demographics of Alzheimer's patients have been greatly studied to better understand risk in different populations. For example, Alzheimer's is usually associated with patients above the age of 65 and twice as prevalent in women compared to men. While these studies help diagnose Alzheimer's, patients with Alzheimer's outside of typical patient demographics may be overlooked. Additionally, while the underlying causes of Alzheimer's is still currently unknown, the underlying pathology may differ in different patient populations. For example, patients with down syndrome are likelier to develop Alzheimer's in their 40s and 50s, a much younger age than the 65+ usually associated with Alzheimer's. As a result, it is important to identify more specific biomarkers for different patient populations. Using machine learning, this project focuses on exploring how feature importance varies in different patient populations leading to improved accuracy of Alzheimer's detection.