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Fig. 2 | BioData Mining

Fig. 2

From: Prediction of the risk of developing end-stage renal diseases in newly diagnosed type 2 diabetes mellitus using artificial intelligence algorithms

Fig. 2

A The feature importance plot and B SHAP summary plot showed the top clinical important features for predicting risks of developing end-stage renal disease in the XGBoost model. Abbreviations: XGBoost, extreme gradient boosting; HSCRP, high-sensitivity C-reactive protein; UPCR, spot urine protein-to-creatinine ratio; ALT, alanine transaminase; DPP4i, dipeptidyl peptidase 4 inhibitors; HGB, hemoglobin; HbA1c, glycated hemoglobin; ALB, albumin; NSAID, nonsteroidal anti-inflammatory drug; HTN, hypertension; INR, international normalized ratio; PI, phosphate

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BioData Mining

ISSN: 1756-0381