Study Findings on Mathematics Published by Researchers at Parahyangan Catholic University (Prediction of Maternity Recovery Rate of Group Long-Term Disability Insurance Using XGBoost): Mathematics

2023 NOV 29 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A new study on mathematics is now available. According to news reporting out of Parahyangan Catholic University by NewsRx editors, research stated, “To help insurers determine insurance rates incorporating maternity factors, it is crucial to understand the maternity recovery rate, which was a metric used by insurance companies to understand how much of the expenses associated with maternity care and related medical services are covered by their policies.”

Our news editors obtained a quote from the research from Parahyangan Catholic University: “This paper employed Extreme Gradient Boosting (XGBoost), a powerful method for handling complex data relationships and preventing overfitting, on North American Group Long-Term Disability dataset obtained from the Society of Actuaries, which listed maternity as one of its categories, to predict the maternity recovery rate. In comparison, other machine learning methods such as Gradient Boosting Machine (GBM) and Bayesian Additive Regression Tree (BART) were used, with Root Mean Squared Error (RMSE) values calculated the difference between predicted and observed maternity recovery rates. Four datasets, 3 imbalanced and 1 fairly-balanced, were created out of the original dataset to test each method’s predictive prowess. The study revealed that XGBoost performed exceptionally well on the imbalanced datasets, while BART showed slight superiority in fairly-balanced data.”

According to the news editors, the research concluded: “Furthermore, the model identified the duration, exposures, and age of participants in both predicting maternity recovery rates and the underwriting process.”

For more information on this research see: Prediction of Maternity Recovery Rate of Group Long-Term Disability Insurance Using XGBoost. JTAM (Jurnal Teori dan Aplikasi Matematika), 2023,7(4):1221-1236. The publisher for JTAM (Jurnal Teori dan Aplikasi Matematika) is Universitas Muhammadiyah Mataram.

A free version of this journal article is available at https://doi.org/10.31764/jtam.v7i4.16825.

Our news journalists report that additional information may be obtained by contacting Felivia Kusnadi, Center for Mathematics and Society, Department of Mathematics, Parahyangan Catholic University, Bandung. Additional authors for this research include Andry Wijaya, Julius Dharma Lesmono.

(Our reports deliver fact-based news of research and discoveries from around the world.)