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

Table 1 Baseline characteristics of patients, n (%)/median (interquartile rage)

Variables	Survival group (n = 165)	Death group (n = 139)	Z/χ2	P value
Sex1			χ2 = 4.53	0.033
Male	106 (64.24)	105 (75.54)
Female	59 (35.76)	34 (24.46)
Age (years)2	65.00 (58.00-71.00)	69.00 (62.50-77.50)	Z = -3.76	< 0.001
Max tumor diameter (cm)2	4.00 (3.00-5.00)	5.00 (4.00-8.00)	Z = -6.32	< 0.001
RBC (× 1012/L)2	4.48 (3.98-4.85)	4.26 (3.65-4.66)	Z = -2.82	0.005
Hemoglobin (g/L)2	136.00 (109.00-148.00)	126.00 (98.00-141.50)	Z = -2.94	0.003
Neutrophils (× 109/L)2	4.07 (3.01-5.56)	4.50 (3.14-6.05)	Z = -1.04	0.298
Monocytes (× 109/L)2	0.30 (0.21-0.43)	0.34 (0.23-0.48)	Z = -1.71	0.087
Lymphocytes (× 109/L)2	1.50 (1.20-1.90)	1.50 (1.00-1.90)	Z = -0.80	0.422
Platelets (× 109/L)2	220.00 (180.00-275.00)	232.00 (194.50-285.00)	Z = -1.16	0.246
Albumin (g/L)2	41.10 (36.30-43.90)	38.60 (34.05-41.95)	Z = -3.40	< 0.001
LDH (U/L)2	302.00 (184.00-406.00)	324.00 (191.00-421.50)	Z = -0.87	0.383
ALT (U/L)2	19.00 (15.00-26.00)	21.00 (14.50-28.00)	Z = -0.79	0.427
AST (U/L)2	20.00 (17.00-26.00)	21.00 (17.00-29.00)	Z = -1.28	0.200
Creatinine (μmol/L)2	67.90 (59.00-78.50)	73.10 (64.10-82.40)	Z = -2.56	0.011
AFP (ng/mL)2	2.63 (1.72-3.50)	2.30 (1.50-3.17)	Z = -1.59	0.112
CEA (ng/mL)2	1.71 (1.03-2.95)	2.65 (1.58-4.67)	Z = -4.34	< 0.001
CA199 (U/mL)2	11.06 (5.89-18.57)	14.95 (7.55-22.47)	Z = -1.90	0.057
Operative time (minutes)2	180.00 (160.00-230.00)	200.00 (170.00-240.00)	Z = -1.58	0.115
Intraoperative blood loss (mL)2	100.00 (100.00-140.00)	146.18 (100.00-200.00)	Z = -5.79	< 0.001
Smoking1			χ2 = 0.98	0.321
No	110 (66.67)	100 (71.94)
Yes	55 (33.33)	39 (28.06)
Drinking1			χ2 = 4.07	0.044
No	111 (67.27)	108 (77.70)
Yes	54 (32.73)	31 (22.30)
HTN1			χ2 = 0.49	0.483
No	120 (72.73)	96 (69.06)
Yes	45 (27.27)	43 (30.94)
DM1			χ2 = 0.05	0.816
No	137 (83.03)	114 (82.01)
Yes	28 (16.97)	25 (17.99)
CHD1			χ2 = 0.84	0.361
No	147 (89.09)	119 (85.61)
Yes	18 (10.91)	20 (14.39)
Abdominal surgery history1			χ2 = 0.18	0.673
No	144 (87.27)	119 (85.61)
Yes	21 (12.73)	20 (14.39)
Resection range1			χ2 = 6.92	0.009
Whole stomach	42 (25.45)	55 (39.57)
Distal and proximal stomachs	123 (74.55)	84 (60.43)
Reconstruction method1			χ2 = 15.33	< 0.001
Billroth I and Billroth II	24 (14.55)	12 (8.63)
Roux-en-Y	100 (60.61)	63 (45.32)
Double-tract	41 (24.85)	64 (46.04)
Complications1			χ2 = 4.21	0.040
No	134 (81.21)	99 (71.22)
Yes	31 (18.79)	40 (28.78)
Lymphovascular invasion1			χ2 = 37.88	< 0.001
No	126 (76.36)	58 (41.73)
Yes	39 (23.64)	81 (58.27)
Nerve infiltration1			χ2 = 14.05	< 0.001
No	124 (75.15)	76 (54.68)
Yes	41 (24.85)	63 (45.32)
Differentiation grade1			χ2 = 5.80	0.055
Highly	13 (7.88)	4 (2.88)
Moderate	66 (40.00)	47 (33.81)
Low and undifferentiated	86 (52.12)	88 (63.31)
Chemotherapy1			χ2 = 0.44	0.506
No	100 (60.61)	79 (56.83)
Yes	65 (39.39)	60 (43.17)
TNM stage1			χ2 = 82.29	< 0.001
I stage	66 (40.00)	7 (5.04)
II stage	31 (18.79)	18 (12.95)
III stage	48 (29.09)	39 (28.06)
IV stage	20 (12.12)	75 (53.96)

¹Categorical variables were expressed as n (%), and inter-group comparisons were conducted using the χ² test.

²Continuous variables that did not follow a normal distribution were expressed as median (interquartile range), and inter-group comparisons were conducted using the Mann-Whitney U test.

RBC: Red blood cell count; LDH: Lactate dehydrogenase; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; AFP: Alpha-fetoprotein; CEA: Carcinoembryonic antigen; CA199: Carbohydrate antigen 19-9; HTN: Hypertension; DM: Diabetes mellitus; CHD: Coronary heart disease; TNM: Tumor-node-metastasis.

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.

Table 3 DeLong test between models

Reference	Comparator	AUC reference	AUC comparator	Delta AUC	Z score	P value
ET	KNN	0.853	0.658	0.195	3.299	< 0.050
ET	SVM	0.853	0.660	0.193	3.285	< 0.050
ET	MLP	0.853	0.760	0.091	2.150	< 0.050
ET	GB	0.853	0.790	0.062	1.884	0.060
ET	XGBoost	0.853	0.808	0.044	1.650	0.098
ET	LR	0.853	0.810	0.043	1.473	0.141
ET	LightGBM	0.853	0.820	0.033	1.370	0.170
ET	RF	0.853	0.817	0.036	1.293	0.196
ET	DT	0.853	0.810	0.042	1.168	0.243

ET: Extremely randomized trees; KNN: K-nearest neighbors; SVM: Support vector machine; MLP: Multi-layer perceptron; GB: Gradient boosting; XGBoost: Extreme gradient boosting; LR: Logistic regression; LightGBM: Light gradient boosting machine; RF: Random forest; DT: Decision tree; AUC: Area under the curve.

Table 4 Comparative analysis of the ten machine learning models

Algorithms	Accuracy	Sensitivity	Precision	Specificity	F1 score
SVM	0.587	0.476	0.556	0.680	0.513
XGBoost	0.696	0.571	0.706	0.700	0.632
LightGBM	0.750	0.738	0.706	0.745	0.729
LR	0.717	0.714	0.682	0.720	0.698
RF	0.707	0.691	0.674	0.720	0.682
MLP	0.663	0.786	0.600	0.560	0.680
DT	0.739	0.810	0.680	0.680	0.739
GB	0.674	0.476	0.714	0.715	0.571
KNN	0.609	0.452	0.594	0.740	0.514
ET	0.772	0.857	0.721	0.760	0.774

SVM: Support vector machine; XGBoost: Extreme gradient boosting; LightGBM: Light gradient boosting machine; LR: Logistic regression; RF: Random forest; MLP: Multi-layer perceptron; DT: Decision tree; GB: Gradient boosting; KNN: K-nearest neighbors; ET: Extremely randomized trees.