Study Results from National Chin-Yi University of Technology in the Area of Mathematics Published (A Study to Identify Long-Term Care Insurance Using Advanced Intelligent RST Hybrid Models with Two-Stage Performance Evaluation): Mathematics

2023 JUL 27 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A new study on mathematics is now available. According to news reporting originating from Taichung, Taiwan, by NewsRx correspondents, research stated, “With the motivation of long-term care 2.0 plans, forecasting models to identify potential customers of long-term care insurance (LTCI) are an important and interesting issue. From the limited literature, most past researchers emphasize traditional statistics techniques to address this issue; however, these are lacking in some areas.”

The news reporters obtained a quote from the research from National Chin-Yi University of Technology: “For example, intelligent hybrid models for LTCI are lacking, performance measurement of components for hybrid models is lacking, and research results for interpretative capacities are lacking, resulting in a black box scenario and difficulty in making decisions, and the gap between identifying potential customers and constructing hybrid models is unbridged. To solve the shortcomings mentioned above, this study proposes some advanced intelligent single and hybrid models; the study object is LTCI customers. The proposed hybrid models were used on the experimental dataset collected from real insurance data and possess the following advantages: (1) The feature selection technique was used to simplify variables for the purpose of improving model performance. (2) The performance of hybrid models was evaluated against some machine learning methods, including rough set theory, decision trees, multilayer perceptron, support vector machine, genetic algorithm, random forest, logistic regression, and naive Bayes, and sensitivity analysis was performed in terms of accuracy, coverage, rules number, and standard deviation. (3) We used the C4.5 algorithm of decision trees and the LEM2 algorithm of rough sets to extract and provide valuably comprehensible decisional rules as decision-making references for the interested parties for their varied benefits. (4) We used post hoc testing to verify the significant difference in groups. Conclusively, this study effectively identifies potential customers for their key attributes and creates a decision rule set of knowledge for use as a reference when solving practical problems by forming a structured solution.”

According to the news editors, the research concluded: “This study is a new trial in the LTCI application field and realizes novel creative application values. Such a hybrid model is rarely seen in identifying LTCI potential customers; thus, the study has sufficient application contribution and managerial benefits to attract much concern from the interested parties.”

For more information on this research see: A Study to Identify Long-Term Care Insurance Using Advanced Intelligent RST Hybrid Models with Two-Stage Performance Evaluation. Mathematics, 2023,11(3010):3010. (Mathematics - http://www.mdpi.com/journal/mathematics). The publisher for Mathematics is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/math11133010.

Our news journalists report that more information may be obtained by contacting You-Shyang Chen, College of Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan. Additional authors for this research include Ying-Hsun Hung, Yu-Sheng Lin.

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