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Life cycle prediction and survival model construction of digital economy enterprises integrating survival analysis

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  • Shulei Yin

Abstract

The life cycle of digital economy enterprises is affected by many complex and nonlinear factors. Traditional methods can only handle some simple linear relationships. This paper proposes an improved gradient boosting regression tree (GBRT) model to further enhance the prediction ability. Firstly, the Kaplan-Meier survival curve is used for descriptive statistics and exploratory analysis. Then, the accelerated failure time (AFT) model is used to model the enterprise life cycle. Finally, the GBRT model is used to predict the mean-square error (MSE) value, and it is compared with the MSE value of the linear regression model. The MSE value of the survival model is 0.015, much smaller than the MSE value of the linear regression model of 0.232, reflecting the superiority of the survival model.

Suggested Citation

  • Shulei Yin, 2025. "Life cycle prediction and survival model construction of digital economy enterprises integrating survival analysis," International Journal of Data Science, Inderscience Enterprises Ltd, vol. 10(6), pages 1-19.
    • Handle: RePEc:ids:ijdsci:v:10:y:2025:i:6:p:1-19
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