Development of a machine learning-based model for predicting postoperative survival in gastric cancer

doi:10.4240/wjgs.v18.i2.114951

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World Journal of Gastrointestinal Surgery

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1948-9366

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

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©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc.

World J Gastrointest Surg. Feb 27, 2026; 18(2): 114951
Published online Feb 27, 2026. doi: 10.4240/wjgs.v18.i2.114951

Table 2 Performance comparison of different imputation methods under various missingness rates

Missing rate (%)	Imputation method	NRMSE	PFC	AUC
5	Mean/mode	0.1290	0.3240	0.6125
	KNN	0.1274	0.3264	0.6167
	MICE	0.1200	0.2994	0.6194
	MissForest	0.1160	0.2635	0.6222
	HDI-MF-Gower	0.1122	0.2471	0.6278
10	Mean/mode	0.1339	0.3236	0.6097
	KNN	0.1368	0.3261	0.6292
	MICE	0.1242	0.3072	0.6319
	MissForest	0.1221	0.2625	0.6514
	HDI-MF-Gower	0.1201	0.2557	0.6568
15	Mean/mode	0.1342	0.3279	0.6208
	KNN	0.1365	0.3211	0.6458
	MICE	0.1261	0.3063	0.6569
	MissForest	0.1227	0.2677	0.6602
	HDI-MF-Gower	0.1202	0.2619	0.6678
20	Mean/mode	0.1413	0.3314	0.6306
	KNN	0.1353	0.3187	0.6361
	MICE	0.1302	0.3098	0.6396
	MissForest	0.1294	0.2755	0.6439
	HDI-MF-Gower	0.1255	0.2650	0.6525

The sample size (n = 240) for this imputation performance evaluation represents the complete-case dataset derived from the original group of 304 patients after removing all samples with any missing values. NRMSE: Normalized root mean square error; KNN: K-nearest neighbors; MICE Multiple imputation by chained equations; PFC: Proportion of falsely classified entries; AUC: Area under the curve.

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Citation: Lü YN, Liu D, Tao S, Wu J, Yu SJ, Yuan HL. Development of a machine learning-based model for predicting postoperative survival in gastric cancer. World J Gastrointest Surg 2026; 18(2): 114951
URL: https://www.wjgnet.com/1948-9366/full/v18/i2/114951.htm
DOI: https://dx.doi.org/10.4240/wjgs.v18.i2.114951

Lü YN, Liu D, Tao S, Wu J, Yu SJ, Yuan HL. Development of a machine learning-based model for predicting postoperative survival in gastric cancer. World J Gastrointest Surg 2026; 18(2): 114951 [PMID: 41809349 DOI: 10.4240/wjgs.v18.i2.114951]

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