Fig. 2
From: Machine learning-based prediction model and visual interpretation for prostate cancer
Ranking of input variables in the XGBoost model to predict prostate cancer (Based on SHAP values). BMI body mass index, ALP alkaline phosphatase, CKMB creatine kinase, fPSA free prostate-specific antigen, tPSA total prostate-specific antigen, f/tPSA free-to-total PSA ratio, Ca calcium, Cl chloride, P inorganic phosphorus, CK creatine kinase, Cre creatinine, UA uric acid, TG triglyceride, HDL-C high density lipoprotein cholesterol, LDL-C low density lipoprotein cholesterol, Apo-A1 Apolipoprotein A1, Apo-B Apolipoprotein B, K potassium