Constructing a prediction model for delayed wound healing after gastric cancer radical surgery based on three machine learning algorithms

Table 1 Clinical and demographic characteristics of the two groups of patients, mean ± SD/n (%)

Variables	Patients
	Total (n = 514)	N group (n = 388)	D group (n = 126)	P value
Preoperative variables
Age, years	63.5 ± 10.1	63.0 ± 10.1	65.2 ± 10.0	0.031
Elderly	23 (4.5)	256 (66)	100 (79.4)	0.005
Sex				0.301
Male	385 (74.9)	295 (76.0)	90 (71.4)
Female	129 (25.1)	93 (24.0)	36 (28.6)
Body mass index, kg/m2	22.6 ± 3.3	22.5 ± 3.4	22.8 ± 3.0	0.505
ASA score				0.320
I	58 (11.3)	48 (12.4)	10 (7.9)
II	343 (66.7)	260 (67.0)	83 (65.9)
III	113 (22.0)	80 (20.6)	33 (26.2)
Hypertension	136 (26.4)	100 (25.8)	36 (28.6)	0.536
Diabetes	42 (8.1)	30 (7.7)	12 (9.5)	0.524
Chronic obstructive pulmonary disease	7 (1.3)	7 (1.8)	0 (0)	0.282
Cardiovascular diseases	43 (8.3)	36 (9.3)	7 (5.6)	0.190
Obesity (body mass index ≥ 30 kg/m2)	6 (1.1)	6 (1.5)	0 (0)	0.354
Underweight (body mass index < 18.5 kg/m2)	119 (23.1)	88 (22.7)	31 (24.6)	0.657
Smoking history	245 (47.6)	187 (48.2)	58 (46.0)	0.673
Drinking history	214 (41.6)	163 (42.0)	51 (40.5)	0.762
History of abdominal surgery	44 (8.5)	30 (7.7)	14 (11.1)	0.239
Emergency	34 (6.6)	28 (7.2)	6 (4.8)	0.335
Weight loss, kg, median (IQR)	2.9 (0, 5.0)	2.0 (0, 5.0)	0 (0, 5.0)	0.534
Hospitalization duration, day	4 (3, 7)	4 (2, 7)	4 (3, 7)	0.497
Total parenteral nutrition	156 (30.3)	124 (32.0)	32 (25.4)	0.164
Hemoglobin concentration, g/L, median (IQR)	123.0 (102.0, 138.0)	124.0 (102.0, 139.0)	120.5 (99.0, 135.8)	0.230
Hypoproteinemia (< 35 g/L)	121 (23.5)	93 (24.0)	28 (22.2)	0.688
Absolute value of neutrophils, 109/L, median (IQR)	3.0 (1.5, 4.5)	3.0 (1.8, 4.7)	2.7 (0.9, 4.0)	0.032
Absolute value of lymphocytes, 109/L, median (IQR)	1.6 (1.2, 2.4)	1.6 (1.2, 2.3)	1.7 (1.2, 2.4)	0.966
Neutrophil to lymphocyte ratio, median (IQR)	2.2 (1.3, 3.6)	2.2 (1.3, 3.5)	2.3 (1.1, 3.7)	0.701
White blood cell count, 109/L	6.8 ± 3.2	6.7 ± 2.7	7.1 ± 4.3	0.329
Blood glucose concentration, mmol/L	6.0 ± 2.1	5.9 ± 1.9	6.3 ± 2.4	0.067
Intraoperative and postoperative variables
Length of incision, cm	9.3 ± 4.8	9.1 ± 4.8	9.1 ± 4.9	0.005
Infection	23 (4.4)	16 (4.1)	7 (5.6)	0.499
Surgical scope				0.158
Complete gastrectomy	128 (24.9)	93 (24.0)	35 (27.7)
Partial gastrectomy	250 (48.6)	198 (51.0)	52 (41.3)
Multi organ combined surgery	136 (26.5)	97 (25.0)	39 (31.0)
Operation				0.505
Laparoscopic	418 (81.4)	313 (80.7)	105 (83.3)
Open surgery	96 (18.6)	75 (19.3)	21 (16.7)
Total input, mL, median (IQR)	2900 (2500, 3500)	2875 (2500, 3500)	3000 (2500, 3500)	0.479
Bleeding, mL, median (IQR)	100.0 (50.0, 150.0)	65.0 (50.0, 150.0)	100.0 (50.0, 187.5)	0.243
Urine output, mL, median (IQR)	400 (250, 600)	400 (250, 600)	450 (300, 650)	0.123
Length of operation, hour	4.3 ± 1.4	4.2 ± 1.4	4.6 ± 1.4	0.005
Destination				0.563
Post anesthesia care unit	181 (35.2)	133 (34.3)	48 (38.1)
Ward	330 (64.2)	252 (64.9)	78 (61.9)
Intensive care unit	3 (0.6)	3 (0.8)	0 (0)
Duration of abdominal drainage, day	12.9 ± 5.1	11.2 ± 3.8	18.0 ± 5.2	< 0.001
Duration of total parenteral nutrition, day, median (IQR)	9.0 (7.0, 11.0)	8.0 (6.0, 10.0)	10.0 (7.0, 12.0)	0.497
C-reactive protein concentration, mg/L, median (IQR)	46.5 (27.0, 78.3)	47.5 (27.0, 76.5)	45.1 (27.9, 79.9)	0.891
Blood glucose concentration, mmol/L	8.1 ± 3.2	8.0 ± 3.1	8.4 ± 3.6	0.247

N: Non delayed healing group; D: Delayed healing group; IQR: Interquartile range; ASA: American Society of Anesthesiologists.

Table 2 Data after feature encoding

Attribute name	Attribute value	Assignment
Sex	Male	1
	Female	0
Elderly (≥ 60 years)	Yes	1
	No	0

Table 3 Patient information the in training and validation sets, mean ± SD/n (%)

Variables	Training set (n = 362)		Validation set (n = 152)
	N group (n = 270)	D group (n = 92)	N group (n = 118)	D group (n = 34)
Elderly (%)	179 (66.3)	72 (78.3)	77 (65.3)	28 (82.4)
Sex (%)
Male	205 (75.9)	68 (73.9)	80 (67.8)	22 (64.7)
Female	65 (24.1)	24 (26.1)	38 (32.2)	12 (35.3)
Absolute value of lymphocytes, 109/L	3.6 ± 2.6	3.1 ± 2.3	3.4 ± 2.6	2.7 ± 2.0
White blood cell count, 109/L	6.7 ± 2.8	7.1 ± 4.7	6.8 ± 2.6	6.9 ± 2.7
Duration of abdominal drainage, day	11.3 ± 3.7	17.8 ± 5.0	10.9 ± 3.9	18.5 ± 5.3

N: Non delayed healing group; D: Delayed healing group.

Table 4 Comparison of the three machine learning models

Datasets	Prediction models	Precision	Accuracy	Recall	F1 index	Area under the receiver operating curve (95%CI)	P value1	P value2
Training set	Decision tree	0.951	0.917	0.937	0.944	0.962 (0.944-0.979)	4.00 × 10-37	0.4233
	Logistic regression	0.848	0.823	0.930	0.887	0.924 (0.897-0.950)	1.40 × 10-30	< 0.0015
	SVM	0.861	0.845	0.944	0.901	0.749 (0.697-0.802)	1.21 × 10-08	< 0.0014
Validation set	Decision tree	0.940	0.901	0.932	0.936	0.951 (0.920-0.979)	2.72 × 10-21	0.0013
	Logistic regression	0.869	0.855	0.957	0.911	0.937 (0.900-0.970)	4.86 × 10-20	< 0.0015
	SVM	0.890	0.875	0.958	0.922	0.773 (0.685-0.855)	7.30 × 10-07	< 0.0014

¹P value represents the significance test results of area under the receiver operating curves performance between the three models and the random model.

²P value indicates the results of significance tests for area under the receiver operating curves comparisons between the models.

³P value comparison between the decision tree and logistic regression.

⁴P value comparison between the decision tree and the support vector machine.

⁵P value comparison between the logistic regression and the support vector machine.

SVM: Support vector machine; CI: Confidence interval.

Table 5 Comparison of performance metrics under the clinical threshold corresponding to the maximized Youden index of the three machine learning models

Datasets	Prediction models	Precision	Accuracy	Recall	F1 index	Youden index	Best threshold
Training set	Decision tree	0.657	0.867	1.000	0.793	0.822	0.130
	Logistic regression	0.641	0.856	0.989	0.778	0.800	0.192
	SVM	0.641	0.856	0.989	0.778	0.800	0.173
Validation set	Decision tree	0.667	0.888	1.000	0.800	0.856	0.219
	Logistic regression	0.611	0.855	0.971	0.750	0.793	0.168
	SVM	0.750	0.921	0.971	0.846	0.877	0.195

SVM: Support vector machine.