Fig. 4: Performance of the machine-learning LASSO model for IRD subtypes classification. | Nature Communications

Fig. 4: Performance of the machine-learning LASSO model for IRD subtypes classification.

From: Metabolomics facilitates differential diagnosis in common inherited retinal degenerations by exploring their profiles of serum metabolites

Fig. 4

a Two diagnostic models were established to differentiate (1) CD/CRD, STGD, and control group and (2) RP with EYS, USH2A, and other genotypes, using the machine learning LASSO model. b The sensitivity and specificity were both 100% in the training and validation sets of the cone-predominant disease diagnosis model. c The area-under-curve (AUC) was 1.0 in the three subgroups in the training and validation set. d The diagnostic accuracy was 83.7% in the training set and 85.7% in the validation set of the RP diagnosis model. e The AUC of the RP diagnosis model for the USH2A, EYS, and other genotypes in the training and validation set. Source data are provided as a Source Data file. LASSO, Least Absolute Shrinkage and Selection Operator, IRD, inherited retinal degeneration, CRD, cone-rod dystrophy, RP, retinitis pigmentosa, STGD, Stargardt disease, AUC, area-under-curve.

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