Performance comparison of different classification algorithms applied to the diagnosis of familial hypercholesterolemia in paediatric subjects.

ABSTRACT: Familial Hypercholesterolemia (FH) is an inherited disorder of lipid metabolism, characterized by increased low density lipoprotein cholesterol (LDLc) levels. The main purpose of the current work was to explore alternative classification methods to traditional clinical criteria for FH diagnosis, based on several biochemical and biological indicators. Logistic regression (LR), decision tree (DT), random forest (RF) and naive Bayes (NB) algorithms were developed for this purpose, and thresholds were optimized by maximization of Youden index (YI). All models presented similar accuracy (Acc), specificity (Spec) and positive predictive values (PPV). Sensitivity (Sens) and G-mean values were significantly higher in LR and RF models, compared to the DT. When compared to Simon Broome (SB) biochemi

SUBMITTER: Albuquerque J 

PROVIDER: S-EPMC8782861 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature