Prognostic prediction models for postoperative patients with stage I to III colorectal cancer based on machine learning

doi:10.4251/wjgo.v16.i12.4597

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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. Dec 15, 2024; 16(12): 4597-4613
Published online Dec 15, 2024. doi: 10.4251/wjgo.v16.i12.4597

Table 1 Comparisons of the mean square error of the four regression methods

Regression methods	MSEs
Regression methods	OS	DFS	RFS	DMFS
Subset regression method	0.3451435	0.2798500	0.2982825	0.2473204
Ridge regression method	0.3446552	0.2851649	0.3134211	0.2618467
LASSO regression method	0.3539798	0.2841859	0.3051014	0.2600286
LASSO cross-validation method	0.3594004	0.3240556	0.3087686	0.2833409

DFS: Disease-free survival; DMFS: Distant metastasis-free survival; LASSO: Least absolute shrinkage and selection operator; MSE: Mean square error; OS: Overall survival; RFS: Recurrence-free survival.

Citation: Ji XL, Xu S, Li XY, Xu JH, Han RS, Guo YJ, Duan LP, Tian ZB. Prognostic prediction models for postoperative patients with stage I to III colorectal cancer based on machine learning. World J Gastrointest Oncol 2024; 16(12): 4597-4613
URL: https://www.wjgnet.com/1948-5204/full/v16/i12/4597.htm
DOI: https://dx.doi.org/10.4251/wjgo.v16.i12.4597