Leveraging machine learning techniques for predicting pancreatic neuroendocrine tumor grades using biochemical and tumor markers

Table 1 General confusion matrix

		Predicted condition
		Predicted negative	Predicted positive
Condition	Condition negative	True negative	False positive
	Condition positive	False negative	True positive

Table 2 Relationship between different pancreatic neuroendocrine tumor grades and clinical variables

	mean ± SD	P value, vs G1	P value, vs G2	P value of binary
Gender (male / female)			s	0.3575
G1	14 / 18
G2	37 / 11	0.002
G3	4 / 7	0.668	0.008
Age				< 0.001
G1	52.47 ± 11.70
G2	49.19 ± 11.34	0.634
G3	50.00 ± 17.04	0.082	0.039
ALT				< 0.001
G1	34.95 ± 72.06
G2	33.97 ± 59.10	0.730
G3	117.02 ± 143.74	0.039	0.006
BIL				< 0.001
G1	9.15 ± 3.65
G2	12.71 ± 9.56	0.009
G3	69.63 ± 67.56	< 0.001	< 0.001
AFP				< 0.001
G1	2.27 ± 1.02
G2	3.34 ± 2.38	0.014
G3	3.47 ± 1.50	0.035	0.606
CEA				< 0.001
G1	1.51 ± 0.82
G2	2.19 ± 2.38	0.132
G3	11.77 ± 17.05	< 0.001	< 0.001
CA19-9				< 0.001
G1	9.58 ± 7.57
G2	20.46 ± 24.74	0.007
G3	37.10 ± 39.40	< 0.001	0.118
CA125				0.0146
G1	10.43 ± 5.60
G2	13.72 ± 12.22	0.039
G3	11.13 ± 4.75	0.942	0.195
CA15-3				< 0.001
G1	8.98 ± 4.34
G2	11.41 ± 5.49	0.361
G3	11.52 ± 3.80	0.585	0.318
CA72-4				< 0.001
G1	1.83 ± 1.43
G2	2.14 ± 1.50	0.217
G3	7.42 ± 7.85	< 0.001	< 0.001

BIL: Bilirubin; ALT: Alanine transaminase; CA: Carbohydrate antigen; CEA: Carcinoembryonic antigen; AFP: Alpha fetoprotein.

Table 3 The highest F1 score, recall rate and precision rate scores of different models

	F1 score	Recall rate	Precision rate
LR	0.80	0.80	0.81
SVM	0.81	0.81	0.82
Linear SVM	0.82	0.82	0.84
LDA-eigen1	0.85	0.85	0.86
LDA-lsqr1	0.85	0.85	0.86
LDA-svd	0.82	0.82	0.84
MLP	0.82	0.81	0.84

¹Values over 0.85. LR: Logistic regression; SVM: Support vector machine; LDA: Linear discriminant analysis; MLP: Multilayer perceptron.

Table 4 F1 score, recall rate and precision rate of each grade in different models

Model	Grade	F1	Recall rate	Precision rate
LR	G1	0.83	0.94	0.75
	G2	0.8	0.73	0.88
	G3	0.73	0.73	0.73
SVM	G1	0.85	0.94	0.77
	G2	0.81	0.75	0.88
	G3	0.73	0.73	0.73
Linear SVM	G1	0.85	0.94	0.77
	G2	0.81	0.73	0.92
	G3	0.8	0.91	0.71
LDA-eigen	G1	0.85	0.94	0.77
	G2	0.85	0.81	0.89
	G3	0.84	0.73	1
LDA-lsqr	G1	0.85	0.94	0.77
	G2	0.85	0.81	0.89
	G3	0.84	0.73	1
LDA-svd	G1	0.85	0.97	0.76
	G2	0.82	0.75	0.9
	G3	0.76	0.73	0.8
LDA-svd	G1	0.85	0.97	0.76
	G2	0.82	0.75	0.9
	G3	0.76	0.73	0.8
MLP	G1	0.83	0.94	0.75
	G2	0.81	0.75	0.88
	G3	0.83	0.94	0.68

LR: Logistic regression; SVM: Support vector machine; LDA: Linear discriminant analysis; MLP: Multilayer perceptron.