A linear regression-based machine learning pipeline for the discovery of clinically relevant correlates of gait speed reserve from multiple physiological systems - Romero-Ortuno, Roman; Knight, Silvin; Kenny, Rose (My University, 2021)
Frailty in older adults is characterized by reduced physiological reserve. Gait speed reserve (GSR: maximum minus usual gait speed) could help identify frailty and act as a proxy for physiological reserve. Utilizing data from 2397 participants aged 50+ from wave 3 of The Irish Longitudinal Study on Ageing, we developed a stepwise linear regression-based machine learning pipeline to select the most important GSR predictors from 34 manually selected features across multiple domains. Variables were selected one at a time such that they maximized the mean adjusted r-squared score from a 5-fold cross-validation. A peak score of (𝟎. 𝟏𝟔 ± 𝟎. 𝟎𝟑) was achieved with 14 variables (giving adjusted-r-squared of 0.18 and 0.16 on 80% training and 20% test data, respectively). The first 7 variables explained 95% of the peak score: grip strength, MOCA cognitive score, third level education, chair stands time, sex, age, and body mass index (BMI). Of the 14 selected features, 11 had statistically significant (p<0.05) effects in the model: sex, MOCA, third level education, chair stands time, age, BMI, grip strength, cardiac output, number of medications, fear of falling, and mean choice reaction time. Associations between GSR and multi-domain features suggest that a network physiology approach is necessary for assessing physiological reserve.
2021 29th European Signal Processing Conference (EUSIPCO)
PUBLISHED
Dublin
Keywords: Gait Speed Reserve; Linear Regression; Machine Learning; Network Physiology; Aging; Frailty; Ageing; Digital Engagement; Age-related disability; Chronic Health Condition; Mobility impairment; Health; Third age/ageing