Prospective Classification of Functional Dependence: Insights from Machine Learning and the Canadian Longitudinal Study on Aging

Purpose: Functional dependence affects well-being and life expectancy. We identified baseline variables that best predict future limitations in basic and instrumental activities of daily living. Method: Using a filtering approach, we selected the best predictors from 4,248 candidate predictors for 39,927 Canadian Longitudinal Study on Aging participants aged 44–88 years and compared machine learning models trained on baseline data (2010–2015) on their ability to classify functional status (independent vs. dependent people) at follow-up (2018–2021) in a training set ( n = 31,941). We then tested the best model in a separate test set ( n = 7,986). Results: Eighteen variables best predicted functional status at follow-up. Logistic regression performed best, achieving 81.9% balanced accuracy in the test set. Protective factors included no baseline limitations, stronger grip, absence of pain or chronic conditions, female sex, having a driver’s license, and good memory. Risk factors included older age, psychological distress, slow walking, retirement, chronic conditions, and physical inactivity. Conclusion: Functional status can be predicted up to 6 years in advance using health, demographic, cognitive, and physical activity variables. Early identification may enable timely interventions to delay functional decline.