The association of dynapenia with whether participants were subject to long-term care prevention or not in the Japanese long-term care insurance system: A pilot study

Iwamura, Masaki; Ando, S.; Yamato, Yosuke; Kajimoto, Hiroyuki; Maeda, Chikako; Shinbo, Kenji; Nanikawa, Wataru; Kumada, Hitoshi

doi:10.15563/jalliedhealthsci.13.1

Cited by 1 publication

(1 citation statement)

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“…The coverage scope of long-term care insurance is "when the insurant loses the self-care life ability, the long-term care insurance can pay regularly for the expense of long-term care", to provide the subsidy on the insurant nursing cost. Examples of related long-term care insurance research topics are: Japanese floods [11], sarcopenia [12], and population ageing [13].…”

Section: Long-term Care Insurancementioning

confidence: 99%

Application of Advanced Hybrid Models to Identify the Sustainable Financial Management Clients of Long-Term Care Insurance Policy

et al. 2022

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The rapid growth of the aging population and the rate of disabled people with physical and mental disorders is increasing the demand for long-term care. The decline in family care could lead to social and economic collapse. In order to reduce the burden of long-term care, long-term care insurance has become one of the most competitive products in the life insurance industry. In the previous literature review, few scholars engaged in the research on this topic with data mining technology, which was motivated to trigger the formation of this study and hoped to increase the different aspects of academic research. The purpose of this study is to develop the long-term insurance business from the original list of insurance clients, to predict whether the sustainable financial management clients will buy the long-term care insurance policies, and to establish a feasible prediction model to assist life insurance companies. This study aims to establish the classified prediction models of Models I~X, to dismantle the data with the percentage split and 10-fold cross validation, plus the application of two kinds of technology as feature selection and data discretization, for the data mining of twenty-three kinds of algorithms in seven different categories (Bayes, Function, Lazy, Meta, Misc, Rule, and Decision Tree) through the data collected from the insurance company database, and to select 20 conditional attributes and 1 decisional attribute (whether to buy the long-term insurance policy or not). The decision attribute is binary classification method for empirical data analysis. The empirical results show that: (1) the marital status, total number of policies purchased, and total amount of policies (including long-term care insurance) are found to be the three important factors affecting the decision attribute; (2) the most stable models are the advanced hybrid Models V and X; and (3) the best classifier is Decision Tree J48 algorithm for the study data used.

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Section: Long-term Care Insurancementioning

confidence: 99%