Prediction of digital transformation of manufacturing industry based on interpretable machine learning

Chen Zhu, Xue Liu and Dong Chen

PLOS ONE, 2024, vol. 19, issue 3, 1-16

Abstract: The enhancement of digital transformation is of paramount importance for business development. This study employs machine learning to establish a predictive model for digital transformation, investigates crucial factors that influence digital transformation, and proposes corresponding improvement strategies. Initially, four commonly used machine learning algorithms are compared, revealing that the Extreme tree classification (ETC) algorithm exhibits the most accurate prediction. Subsequently, through correlation analysis and recursive elimination, key features that impact digital transformation are selected resulting in the corresponding feature subset. Shapley Additive Explanation (SHAP) values are then employed to perform an interpretable analysis on the predictive model, elucidating the effects of each key feature on digital transformation and obtaining critical feature values. Lastly, informed by practical considerations, we propose a quantitative adjustment strategy to enhance the degree of digital transformation in enterprises, which provides guidance for digital development.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0299147

DOI: 10.1371/journal.pone.0299147

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