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

Abstract

BackgroundStudies 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/MethodsThe 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.ResultsHealth-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).ConclusionsMachine 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.

Impacts of Anthropometric, Biochemical, Socio-Demographic, and Dietary Habits Factors on the Health Status of Urban Corporate People in a Developing Country

“…Anthropometric measurements such as weight, height, body mass index (BMI), body circumference (arm, waist, hip, and calf), waist to hip ratio (WHR), elbow amplitude and knee–heel length, etc., are highly related to the health status of an individual [ 20 ]. They reflect the health status of an individual and also predict the overall performance of the health and survival of an individual [ 21 ].…”

Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices

“…ML is becoming a popular and efficient approach to evaluate multidimensional longitudinal health data in different fields of medical research. Examples of this kind of studies include the diagnosis of asymptomatic liver disease [ 16 ], the prediction of opioid dependence [ 17 ], the evaluation of sociodemographic determinants of health status in aging [ 18 ], the prediction of the mobility of medical rescue-vehicles [ 19 ], forecasting adverse perioperative outcomes [ 20 ], the measure of caloric intake at the population level [ 21 ], the personalisation of oncological treatment in radiogenomics [ 22 ], the determination of features of systolic blood pressure variability [ 23 ], the identification of clinical variables in bipolar disorder [ 24 ] and, interestingly, a specific interest in uncovering potential predictors of diabetes (type 1 and 2) using large set of data [ 25 – 32 ]. ML can also support global efforts in various fields of epidemic outbreaks of infectious diseases, developing up-to-date text and data-mining techniques to assist COVID-19-related research, especially by developing drugs faster (screening and detecting antibody virus interactions and detect viral antigens), understanding viruses better, mapping where viruses come from, and hopefully predicting the next pandemic [ 33 , 34 ].…”

Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices

“…ML is becoming a popular and efficient approach to evaluate multidimensional longitudinal health data in different fields of medical research. Examples of this kind of studies include the diagnosis of asymptomatic liver disease [16], the prediction of opioid dependence [17], the evaluation of sociodemographic determinants of health status in aging [18], the prediction of the mobility of medical rescue-vehicles [19], forecasting adverse perioperative outcomes [20], the measure of caloric intake at the population level [21], the personalisation of oncological treatment in radiogenomics [22], the determination of features of systolic blood pressure variability [23], the identification of clinical variables in bipolar disorder [24] and, interestingly, a specific interest in uncovering potential predictors of diabetes (type 1 and 2) using large set of data [25][26][27][28][29][30][31][32]. ML can also support global efforts in various fields of epidemic outbreaks of infectious diseases, developing up-to-date text and data-mining techniques to assist COVID-19-related research, especially by developing drugs faster (screening and detecting antibody virus interactions and detect viral antigens), understanding viruses better, mapping where viruses come from, and hopefully predicting the next pandemic [33,34].…”