Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer

Figure 4 Construction of synchronous liver metastasis prediction model via random forest model. A: The application prediction model formula of random forest model (RFM) 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: The gravel plot indicates the robustness of the RFM prediction model. IND: Inverse difference; MES: Mean sum; SUV: Sum variance; SUE: Sum entropy; DIV: Difference variance; DIE: Difference entropy; RFM: Random forest model; ANNM: Artificial neural network model; GLRM: Generalized linear regression model; SLM: Synchronous liver metastasis; BMI: Body mass index; AST: Aspartate aminotransferase; ALT: Alanine aminotransferase; CEA: Carcinoembryonic antigen; CA199: Carbohydrate antigen 199; AFP: Alpha-fetoprotein; NI: Neural infiltration; VI: Vascular invasion; HbsAg: Hepatitis B surface antigen.