Development and validation of a postoperative pulmonary infection prediction model for patients with primary hepatic carcinoma

Figure 4 Construction of pulmonary infection prediction model via the random forest model and artificial neural network model. A: Random forest model. The application prediction model formula of the random forest model is as follows: C = argmax (∑(Ci)), where Ci represents the type of in prediction for the i-th tree, C is the final classification result, and I is the number of trees; B: Artificial neural network model. The formula of the artificial neural network model is as follows: θ = θ−η × ▽ (θ). J (θ) among them η is the learning rate, so (θ). J (θ) represents the gradient change of the loss function [i.e., J(θ)]. AFP: Alpha-fetoprotein; BMI: Body mass index; DIE: Difference entropy; DIV: Difference variance; HBV: Hepatitis B virus; IND: Inverse difference; LNM: Lymph node metastasis; MES: Mean sum; SOS: Sum of squares; SUE: Sum entropy; SUV: Sum variance.