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

Figure 3 The impact of a combination of independent variables and change of parameter on F1 score, recall rate and precision rate of four models. Combination 1: bilirubin (BIL); 2: BIL + alanine transaminase (ALT); 3: BIL + ALT + carbohydrate antigen 72-4 (CA72-4); 4: BIL + ALT + CA72-4 + carcinoembryonic antigen (CEA); 5: BIL + ALT + CA72-4 + CEA + CA19-9; 6: BIL + ALT + CA72-4 + CEA + CA19-9 + alpha fetoprotein (AFP); 7: BIL + ALT + CA72-4 + CEA + CA19-9 + AFP + age; 8: BIL + ALT + CA72-4 + CEA + CA19-9 + AFP + age + CA15-3; 9: BIL + ALT + CA72-4 + CEA + CA19-9 + AFP + age + CA15-3 + CA125; 10: BIL + ALT + CA72-4 + CEA + CA19-9 + AFP + age + CA15-3 + CA125 + gender). LR: Logistic regression; SVM: Support vector machine; LDA: Linear discriminant analysis MLP: Multilayer perceptron.