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A hybrid FSRF model based on regression algorithm for diabetes medical expense prediction

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  • Luo, Min
  • Xiao, Fei
  • Chen, Zi-yu
  • Wang, Xiao-kang
  • Hou, Wen-hui
  • Wang, Jian-qiang

Abstract

The number of patients with diabetes continues to grow, and the expense of treating diabetes is enormous. Therefore, predicting medical expenses for diabetes has become a priority in many countries. This paper proposes a new hybrid FSRF model to predict medical expenses. Firstly, in response to the problem of multiple features in medical data, we use a random forest (RF) feature extraction algorithm for feature extraction. Secondly, for complex medical concepts, we develop an improved multi-granularity embedding model for encoding medical concepts. Next, we establish the FA-SSA by optimizing the sparrow search algorithm (SSA) using the firefly algorithm (FA). Then, we employ the FA-SSA algorithm to optimize the parameters of the RF model with multi-granularity medical concept embedding. Finally, we build an improved FSRF model and conduct a case study on a medical dataset in Pingjiang County. This paper performs ablation experiments and four sets of comparative experiments, and the experimental results show the superiority of the FSRF model.

Suggested Citation

  • Luo, Min & Xiao, Fei & Chen, Zi-yu & Wang, Xiao-kang & Hou, Wen-hui & Wang, Jian-qiang, 2024. "A hybrid FSRF model based on regression algorithm for diabetes medical expense prediction," Technological Forecasting and Social Change, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:tefoso:v:207:y:2024:i:c:s0040162524004323
    DOI: 10.1016/j.techfore.2024.123634
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    References listed on IDEAS

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    1. Alexandre Vimont & Henri Leleu & Isabelle Durand-Zaleski, 2022. "Machine learning versus regression modelling in predicting individual healthcare costs from a representative sample of the nationwide claims database in France," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(2), pages 211-223, March.
    2. Hao, Siyuan, 2023. "Modeling hospitalization medical expenditure of the elderly in China," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 450-461.
    3. Chen, Shuixia & Wang, Jian-qiang & Zhang, Hong-yu, 2019. "A hybrid PSO-SVM model based on clustering algorithm for short-term atmospheric pollutant concentration forecasting," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 41-54.
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