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

被引：1

作者：

Chen, You-Shyang ^{[1
]}

Lin, Chien-Ku ^{[2
]}

Chou, Jerome Chih-Lung ^{[3
]}

Chen, Su-Fen ^{[4
,5
]}

Ting, Min-Hui ^{[6
]}

机构：

[1] Natl Chin Yi Univ Technol, Coll Management, Taichung 411, Taiwan

[2] Hsiuping Univ Sci & Technol, Dept Business Management, Taichung 412, Taiwan

[3] Hwa Hsia Univ Technol, Dept Informat Management, New Taipei 235, Taiwan

[4] Natl Open Univ, Dept Management & Informat, New Taipei 247, Taiwan

[5] Natl Open Univ, Dept Business, New Taipei 247, Taiwan

[6] Chang Gung Univ, Grad Inst Management, Taoyuan 333, Taiwan

来源：

SUSTAINABILITY | 2022年 / 14卷 / 19期

关键词：

long-term care; feature selection; data discretization; data mining; sustainable financial management; DECISION; ALGORITHM; SYSTEM;

D O I：

10.3390/su141912485

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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.

引用

页数：25