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Healthcare Cost Prediction Based on Hybrid Machine Learning Algorithms

Author

Listed:
  • 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)

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.

Suggested Citation

  • Shujie Zou & Chiawei Chu & Ning Shen & Jia Ren, 2023. "Healthcare Cost Prediction Based on Hybrid Machine Learning Algorithms," Mathematics, MDPI, vol. 11(23), pages 1-13, November.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:23:p:4778-:d:1288440
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    References listed on IDEAS

    as
    1. Lianjie Wang & Yao Tang & Farnaz Roshanmehr & Xiao Bai & Farzad Taghizadeh-Hesary & Farhad Taghizadeh-Hesary, 2021. "The Health Status Transition and Medical Expenditure Evaluation of Elderly Population in China," IJERPH, MDPI, vol. 18(13), pages 1-14, June.
    2. Junqiang Han & Xiaodong Zhang & Yingying Meng, 2020. "The Impact of Internet Medical Information Overflow on Residents’ Medical Expenditure Based on China’s Observations," IJERPH, MDPI, vol. 17(10), pages 1-16, May.
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