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

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.