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Generalized hurdle count data models based on interpretable machine learning with an application to health care demand

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Abstract

The zero-inflated count data model has long been viewed as an important research topic owing to its enormously different disciplines. As early classical statistical models of linear and logarithmic mean transformation are difficult to be consistent with reality, an enhanced hurdle model based on machine learning methods is proposed. The decision tree, random forest, support vector, and XGBoost methods are introduced in the two stages of the hurdle model. This framework allows to capture the decision-making behavior and predict the count more flexibly and accurately. The generalized hurdle model consists of traditional discrete distributions, which can fit under-dispersed, equi-dispersed, or over-dispersed count data. The extended hurdle models are utilized to fit health care data and compare their performance with traditional count models. The results show that the generalized hurdle model with random forest performs best. Variable importance, break-down plots, and partial plots provide better interpretability for the extended model, which makes the results more reliable and transparent. To the best of our knowledge, this is the first study to generalize the hurdle model with interpretable machine learning methods in count data.

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Acknowledgements

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by XX, TY, JG and DC. The first draft of the manuscript was written by XX and DC, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

Xin Xu acknowledges financial support from the National Social Science Foundation of China (22BTJ016).

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Author notes

  1. Tao Ye, Jieying Gao and Dongxiao Chu have contributed equally to this work.

Authors and Affiliations

  1. School of Finance, Capital University of Economics and Business, Beijing, 100070, China

    Xin Xu, Jieying Gao & Dongxiao Chu

  2. School of Banking and Finance, University of International Business and Economics, Beijing, 100029, China

    Tao Ye

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  1. Xin Xu
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  2. Tao Ye
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  3. Jieying Gao
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Correspondence to Dongxiao Chu.

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Xu, X., Ye, T., Gao, J. et al. Generalized hurdle count data models based on interpretable machine learning with an application to health care demand. Computing 106, 295–325 (2024). https://doi.org/10.1007/s00607-023-01224-3

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