Machine learning for modeling and identifying risk factors of pancreatic fistula

doi:10.4251/wjgo.v17.i4.100089

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Apr 15, 2025 (publication date) through Mar 28, 2025

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World Journal of Gastrointestinal Oncology

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1948-5204

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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World J Gastrointest Oncol. Apr 15, 2025; 17(4): 100089
Published online Apr 15, 2025. doi: 10.4251/wjgo.v17.i4.100089

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Figure 2 Feature importance. A: Binary pre- and intraoperative model of pancreatic fistula; B: Binary postoperative model of pancreatic fistula; C: Three-dimensional pre- and intraoperative model of pancreatic fistula; D: Three-dimensional postoperative model of pancreatic fistula. Importances of the parameters (%), are presented at the abscissa. The features are ranked by importance. Im: Importance; BMI: Body mass index.

Citation: Potievskiy MB, Petrov LO, Ivanov SA, Sokolov PV, Trifanov VS, Grishin NA, Moshurov RI, Shegai PV, Kaprin AD. Machine learning for modeling and identifying risk factors of pancreatic fistula. World J Gastrointest Oncol 2025; 17(4): 100089
URL: https://www.wjgnet.com/1948-5204/full/v17/i4/100089.htm
DOI: https://dx.doi.org/10.4251/wjgo.v17.i4.100089