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.

Davis, J., Knight, S.P., Rizzo, R., Donoghue, O.A., Kenny, R.A., Romero-Ortuno, R. (2021). A linear regression-based machine learning pipeline for the discovery of clinically relevant correlates of gait speed reserve from multiple physiological systems. In 2021 29th European Signal Processing Conference (EUSIPCO) (pp. 1266-1270). PO BOX 74251, KESSARIANI, 151 10, GREECE : EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP [10.23919/EUSIPCO54536.2021.9616187].

A linear regression-based machine learning pipeline for the discovery of clinically relevant correlates of gait speed reserve from multiple physiological systems

Rizzo, R;
2021-01-01

Abstract

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.
978-9-0827-9706-0
Davis, J., Knight, S.P., Rizzo, R., Donoghue, O.A., Kenny, R.A., Romero-Ortuno, R. (2021). A linear regression-based machine learning pipeline for the discovery of clinically relevant correlates of gait speed reserve from multiple physiological systems. In 2021 29th European Signal Processing Conference (EUSIPCO) (pp. 1266-1270). PO BOX 74251, KESSARIANI, 151 10, GREECE : EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP [10.23919/EUSIPCO54536.2021.9616187].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/576854
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