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eISSN: 1643-3750

Sociodemographic Indicators of Health Status Using a Machine Learning Approach and Data from the English Longitudinal Study of Aging (ELSA)

Worrawat Engchuan, Alexandros C. Dimopoulos, Stefanos Tyrovolas, Francisco Félix Caballero, Albert Sanchez-Niubo, Holger Arndt, Jose Luis Ayuso-Mateos, Josep Maria Haro, Somnath Chatterji, Demosthenes B. Panagiotakos

(The Centre for Applied Genomics, Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada)

Med Sci Monit 2019; 25:1994-2001

DOI: 10.12659/MSM.913283

Published: 2019-03-17


BACKGROUND: Studies on the effects of sociodemographic factors on health in aging now include the use of statistical models and machine learning. The aim of this study was to evaluate the determinants of health in aging using machine learning methods and to compare the accuracy with traditional methods.
MATERIAL AND METHODS: The health status of 6,209 adults, age <65 years (n=1,585), 65-79 years (n=3,267), and >80 years (n=1,357) were measured using an established health metric (0-100) that incorporated physical function and activities of daily living (ADL). Data from the English Longitudinal Study of Ageing (ELSA) included socio-economic and sociodemographic characteristics and history of falls. Health-trend and personal-fitted variables were generated as predictors of health metrics using three machine learning methods, random forest (RF), deep learning (DL) and the linear model (LM), with calculation of the percentage increase in mean square error (%IncMSE) as a measure of the importance of a given predictive variable, when the variable was removed from the model.
RESULTS: Health-trend, physical activity, and personal-fitted variables were the main predictors of health, with the%incMSE of 85.76%, 63.40%, and 46.71%, respectively. Age, employment status, alcohol consumption, and household income had the%incMSE of 20.40%, 20.10%, 16.94%, and 13.61%, respectively. Performance of the RF method was similar to the traditional LM (p=0.7), but RF significantly outperformed DL (p=0.006).
CONCLUSIONS: Machine learning methods can be used to evaluate multidimensional longitudinal health data and may provide accurate results with fewer requirements when compared with traditional statistical modeling.

Keywords: Artificial Intelligence, Data Interpretation, Statistical, Decision Support Techniques, Socioeconomic Factors



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