Machine learning model for predicting hospital-acquired functional decline in older patients with postoperative cardiovascular surgery

doi:10.12998/wjcc.v14.i4.117700

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World Journal of Clinical Cases

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2307-8960

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

Retrospective Study

World J Clin Cases. Feb 6, 2026; 14(4): 117700
Published online Feb 6, 2026. doi: 10.12998/wjcc.v14.i4.117700

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Figure 2 Study flowchart of hospital-acquired functional decline incidence among study participants. A total of 144 patients were enrolled in the study, with 41 (28.5%) developing hospital-acquired functional decline (HAFD). The remaining 103 patients were in the non-HAFD group. AdaBoost: Adaptive boosting model; CatBoost: Category boosting model; SVM: Support vector machine model; XGBoost: Extreme gradient boosting model.

Citation: Hiramatsu R, Imaoka S, Minata S, Sako H, Sato N. Machine learning model for predicting hospital-acquired functional decline in older patients with postoperative cardiovascular surgery. World J Clin Cases 2026; 14(4): 117700
URL: https://www.wjgnet.com/2307-8960/full/v14/i4/117700.htm
DOI: https://dx.doi.org/10.12998/wjcc.v14.i4.117700

Hiramatsu R, Imaoka S, Minata S, Sako H, Sato N. Machine learning model for predicting hospital-acquired functional decline in older patients with postoperative cardiovascular surgery. World J Clin Cases 2026; 14(4): 117700 [PMID: 41695763 DOI: 10.12998/wjcc.v14.i4.117700]

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