Promoting Equity: Assessing Fairness in Approaches for Predicting Alzheimer's Disease

Overview

Alzheimer's disease (AD) presents a significant challenge for older adults in marginalized communities. The application of machine learning (ML) techniques through predictive models holds promise for enhancing the early detection and management of AD. However, it is essential to address potential biases within ML models that may contribute to or exacerbate existing disparities. This study delves into the algorithmic fairness of four distinct ML models, logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), and multilayer perceptron (MLP), in predicting the Alzheimer's disease. The assessment of fairness spans gender, ethnicity, and race subgroups, employing three key measures: demographic parity, equalized odds, and disparate impact. While all four ML models exhibited strong overall performance, disparities emerged across race and ethnicity subgroups. Hispanic participants experienced lower sensitivity compared to their Non-Hispanic counterparts. Similar patterns of decreased sensitivity were observed for non-white participants in contrast to Non-Hispanic White individuals. Inclusion of demographic features to the model led to a drop of prediction performance. Despite their aggregate accuracy, the ML models fell short in meeting fairness metrics, emphasizing the need to integrate fairness considerations in both the development and deployment phases of ML models for AD prediction. Addressing these disparities is crucial for ensuring equitable outcomes in the utilization of predictive models within diverse populations.

Competition history

• AJAS 2025 Category not listed

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