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

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

Technological Forecasting and Social Change, 2024, vol. 207, issue C

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.

Keywords: Machine learning; Sparrow search algorithm; Firefly algorithm; Random forest; Medical concepts (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:207:y:2024:i:c:s0040162524004323

DOI: 10.1016/j.techfore.2024.123634

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