Fig. 1: Study design and performance of 12 machine learning (ML) models in the discovery cohort. | Nature Communications

Fig. 1: Study design and performance of 12 machine learning (ML) models in the discovery cohort.

From: A noninvasive machine learning model using a complete blood count for screening of primary vitreoretinal lymphoma

Fig. 1: Study design and performance of 12 machine learning (ML) models in the discovery cohort.

A Schematic representation of the study design, illustrating the development and validation of a ML model for primary vitreoretinal lymphoma (PVRL) diagnosis using complete blood count parameters. B Receiver operating characteristic (ROC) curves comparing the diagnostic performance of 12 machine learning models on the basis of complete blood count data. C Summary of key performance metrics for the 12 machine learning models, including area under the curve (AUC), sensitivity, and specificity. RF random forest, DT decision tree, GLM generalized linear model, GBM gradient boosting, KNN K nearest neighbor, PDW platelet distribution width, PLCR platelet large cell ratio, HG hemoglobin, CNS central nervous system, PPV positive predictive value, NPV negative predictive value.

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