• The Journal of Industrial Distribution & Business
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• v.11 no.7
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• pp.29-40
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• 2020

Purpose: In modern society, many urban problems are occurring, such as aging, hollowing out old city centers and polarization within cities. In this study, we intend to apply big data and machine learning methodologies to predict depression symptoms in the elderly population early on, thus contributing to solving the problem of elderly depression. Research design, data and methodology: Machine learning techniques used random forest and analyzed the correlation between CES-D10 and other variables, which are widely used worldwide, to estimate important variables. Dependent variables were set up as two variables that distinguish normal/depression from moderate/severe depression, and a total of 106 independent variables were included, including subjective health conditions, cognitive abilities, and daily life quality surveys, as well as the objective characteristics of the elderly as well as the subjective health, health, employment, household background, income, consumption, assets, subjective expectations, and quality of life surveys. Results: Studies have shown that satisfaction with residential areas and quality of life and cognitive ability scores have important effects in classifying elderly depression, satisfaction with living quality and economic conditions, and number of outpatient care in living areas and clinics have been important variables. In addition, the results of a random forest performance evaluation, the accuracy of classification model that classify whether elderly depression or not was 86.3%, the sensitivity 79.5%, and the specificity 93.3%. And the accuracy of classification model the degree of elderly depression was 86.1%, sensitivity 93.9% and specificity 74.7%. Conclusions: In this study, the important variables of the estimated predictive model were identified using the random forest technique and the study was conducted with a focus on the predictive performance itself. Although there are limitations in research, such as the lack of clear criteria for the classification of depression levels and the failure to reflect variables other than KLoSA data, it is expected that if additional variables are secured in the future and high-performance predictive models are estimated and utilized through various machine learning techniques, it will be able to consider ways to improve the quality of life of senior citizens through early detection of depression and thus help them make public policy decisions.

• Korea journal of population studies
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• v.11 no.1
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• pp.159-184
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• 1988

Race and ethnicity are important factors which influence the elderIy's residential adjustment behaviors, although it is unclear whether this reflects influences unrelated to race and ethnlcity. Culturally, the norm of family supportoften obseved among various minority ethnic groups is likely to provide flexible family suppof for the elderIy. Economically, the life-long hardship ofminority groups is likely to force them to maintain extended family living arrangements simply to reduce expenses via economies of scale. Thecontroversy about the economic need versus the cultural prescription forextended living arrangements remains unresoIved because it fails to articulatethe meaning of family supports among many disadvantaged groups.This study aims to test previous economic and cultural arguments, byexamining ethnic differences iu the eiderIy's responsiveness to their health andeconomic problems. Two hypotheses about cultural influences on the elderly's resideutiai adjustment are examined. First, do elderly minorities receive famiiysupporis for longer periods when they are poor if economic and health status\ulcorner Second, do elderiy minorities receive family supports more often when their health status declines\ulcorner Using the Longitudinal Suvey on Aging from 1984 to 1990. this study employs Markovian multi-state life tables, and discrete and contonuous competing hazard analyses for the transition in living arrangements. The main results provide substantial evidence against the cultural resource thesis. Elderly minorities experience more frequent transition between living alone and living with relatives than white elderly persons when group differences in the extent of mortality and insititutionalization are controlled. The shorter timf of living alone among elderly monorities stems from their greater likehood of joining relatives as well as greater mortality and attrition rates than elderly whites. Coresidence of elderly whites with their relatives is more likely to occur in response to their needs for health care than of elderly whites. it implies that instability. not flezibility. characterrizes elderly minorities living arrangements.