Machine learning-based comparison of factors influencing estimated glomerular filtration rate in Chinese women with or without non-alcoholic fatty liver

Figure 1 Flowchart of sample selection from the MJ Health Screening Centers Study Cohort.

Figure 2 Flowchart of the proposed machine learning methods. XGBoost: eXtreme gradient boosting.

Figure 3 Equation of performance evaluation metrics. ŷ_i and y_i respectively represent predicted and actual values; n is the number of instances. SMAPE: Symmetric Mean Absolute Percentage Error; MAPE: Mean Absolute Percentage Error; RAE: Relative Absolute Error; RRSE: Root Relative Squared Error; RMSE: Root Mean Squared Error.

Figure 4 All the orders of the ranks of percentage importance were the same as the results in the tables. A: The average of the risk factors derived from three different machine learning methods (Model 1, NAFLD+ with age); B: The ranks of the risk factors derived from three different machine learning methods (Model 1, NAFLD- with age); C: The ranks of the risk factors derived from three different machine learning methods (Model 2, NAFLD+ without age); D: The ranks of the risk factors derived from three different machine learning methods (Model 2, NAFLD- without age). NAFLD: Non-alcoholic fatty liver.