Modeling the Importance of Life Exposure Factors on Memory Performance in Diverse Older Adults: A Machine Learning Approach

Authors

Evan Fletcher
Marianne Chanti-Ketterl
Emily Hokett
Yi Lor
Umesh Venkatesan, Thomas Jefferson UniversityFollow
Ruijia Chen
Omonigho M. Bubu
Rachel Whitmer
Paola Gilsanz
Zvinka Z. Zlatar

Document Type

Article

Publication Date

8-20-2025

Comments

This article is the author’s final published version in Alzheimer's and Dementia, Volume 21, Issue 8, 2025, Article number e70428.

The published version is available at https://doi.org/10.1002/alz.704281. Copyright © 2025 The Author(s).

Abstract

INTRODUCTION: Many health life exposure factors (LEFs) influence cognitive decline and dementia incidence, but their relative importance to episodic memory (an early indicator of cognitive decline) among diverse older adults is unclear. We used machine learning to rank LEFs for memory performance in a large and diverse US cohort.

METHODS: Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE) and Study of Healthy Aging in African Americans (STAR), participants underwent neuropsychological testing and answered questionnaires about multiple LEFs. XGBoost and Shapley Additive exPlanation values ranked the importance of factors influencing cross-sectional episodic memory in the full sample and by sex and ethnic group.

RESULTS: Among 2245 adults (mean age: 74 years; range 54-90), age, sex, education, volunteering, income, vision, hearing, sleep, and exercise contributed to memory performance regardless of group stratification.

DISCUSSION: This innovative methodology can help identify risk factors important for memory performance and guide future dementia risk reduction interventions among older adults.

HIGHLIGHTS: This work uses a regression tree machine learning model (XGBoost) with highly interpretable Shapley Additive exPlanation values to analyze impacts of 12 life exposure factors plus age, sex and ethnoracial identity on episodic memory outcome. This approach has valuable properties, including the ability to implicitly account for variable interactions, non-linear relations with outcome, and missing values. Age, sex, education, income, volunteering, exercise, hearing and vision, and sleep (quality and duration) have important impacts on memory outcome in a combined model and in stratified models regardless of ethnoracial identity. We also demonstrate individualized models for subgroups of participants, showing how life exposure factors vary in importance between divergent populations and suggesting an approach to personalized interventions. This approach can be valuable for both policy decisions and individualized interventions to support healthy cognitive aging.

Recommended Citation

Fletcher, Evan; Chanti-Ketterl, Marianne; Hokett, Emily; Lor, Yi; Venkatesan, Umesh; Chen, Ruijia; Bubu, Omonigho M.; Whitmer, Rachel; Gilsanz, Paola; and Zlatar, Zvinka Z., "Modeling the Importance of Life Exposure Factors on Memory Performance in Diverse Older Adults: A Machine Learning Approach" (2025). Moss-Magee Rehabilitation Papers. Paper 18.
https://jdc.jefferson.edu/mossrehabfp/18

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Language

English