Healthcare Cost Prediction Based on Hybrid Machine Learning Algorithms

Shujie Zou, Chiawei Chu (), Ning Shen and Jia Ren
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Shujie Zou: Faculty of Data Science, City University of Macau, Macau 999078, China
Chiawei Chu: Faculty of Data Science, City University of Macau, Macau 999078, China
Ning Shen: Department of Innovation, Technology and Entrepreneurship, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
Jia Ren: School of Information and Communication Engineering, Hainan University, Haikou 570100, China

Mathematics, 2023, vol. 11, issue 23, 1-13

Abstract: Healthcare cost is an issue of concern right now. While many complex machine learning algorithms have been proposed to analyze healthcare cost and address the shortcomings of linear regression and reliance on expert analyses, these algorithms do not take into account whether each characteristic variable contained in the healthcare data has a positive effect on predicting healthcare cost. This paper uses hybrid machine learning algorithms to predict healthcare cost. First, network structure learning algorithms (a score-based algorithm, constraint-based algorithm, and hybrid algorithm) for a Conditional Gaussian Bayesian Network (CGBN) are used to learn the isolated characteristic variables in healthcare data without changing the data properties (i.e., discrete or continuous). Then, the isolated characteristic variables are removed from the original data and the remaining data used to train regression algorithms. Two public healthcare datasets are used to test the performance of the proposed hybrid machine learning algorithm model. Experiments show that when compared to popular single machine learning algorithms (Long Short Term Memory, Random Forest, etc.) the proposed scheme can obtain similar or higher prediction accuracy with a reduced amount of data.

Keywords: healthcare costs; CGBN; regression algorithm; hybrid algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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