Integrating Machine Learning and Environmental and Genetic Risk Factors for the Early Detection of Preclinical Alzheimer's Disease.

Abstract

Objective: This study classified preclinical Alzheimer's disease (AD) using cognitive screening, neighborhood deprivation via the Area Deprivation Index (ADI), and sociodemographic and genetic risk factors. Additionally, it compared the predictive accuracy of multiple machine learning algorithms and examined model performance with two bootstrapping procedures.

Methods: Data were drawn from a longitudinal cohort that required participants to be age 65 or older, cognitively normal at baseline, and active drivers, defined as taking at least one trip a week. Naturalistic driving data were collected using a commercial datalogger. Biomarker positivity was determined via amyloid pathology using cerebrospinal fluid and positron emission tomography imaging. ADI was captured based on geocoding latitude and longitude to derive a national ranking for the specific location (home or unique destination). Machine learning algorithms classified preclinical AD. Each individual model's predictive ability was confirmed in a 20% testing dataset with 100 rounds of resampling with and without replacement.

Results: Among 292 participants (n = 2,792 observations), including ADI of trip destinations, participants' home ADI, and frequency of trips to the same ADI led to a slight but notable improvement in predicting preclinical AD. The ensemble model demonstrated superior predictive performance, highlighting the potential of integrating multiple models for early AD detection.

Discussion: Our findings underscore the importance of incorporating socioeconomic and environmental variables, such as neighborhood deprivation, in predicting preclinical AD. Addressing socioeconomic disparities through public health strategies is crucial for mitigating AD risk and enhancing the quality of life for older adults.