Fig. 6 | Scientific Reports

Fig. 6

From: Identification and validation of inflammatory response genes linking chronic kidney disease with coronary artery disease based on bioinformatics and machine learning

Fig. 6

Identification of potential diagnostic genes for CKD related CAD via two machine learning methods. (A, B) The LASSO logistic regression algorithm was applied to determine the minimum and lambda values of diagnostic genes in CAD. (C) MeanDecreaseGini analysis was performed on 14 genes using the RF algorithm, and nine biomarkers with scores greater than 2 were selected in CAD. (D, E) The LASSO logistic regression algorithm was applied to determine the minimum and lambda values of diagnostic genes in CKD. (F) MeanDecreaseGini analysis was performed on 14 genes using the RF algorithm, and ten biomarkers with scores greater than 2 were selected in CKD. (G) Venn diagram showing the overlap in results between two algorithms. CKD, chronic kidney disease; CAD, coronary artery disease; LASSO, least absolute shrinkage and selection operator; RF, random forest.

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