Comparative study on the performance of different classification algorithms, combined with pre- and post-processing techniques to handle imbalanced data, in the diagnosis of adult patients with familial hypercholesterolemia.

ABSTRACT: Familial Hypercholesterolemia (FH) is an inherited disorder of cholesterol metabolism. Current criteria for FH diagnosis, like Simon Broome (SB) criteria, lead to high false positive rates. The aim of this work was to explore alternative classification procedures for FH diagnosis, based on different biological and biochemical indicators. For this purpose, logistic regression (LR), naive Bayes classifier (NB), random forest (RF) and extreme gradient boosting (XGB) algorithms were combined with Synthetic Minority Oversampling Technique (SMOTE), or threshold adjustment by maximizing Youden index (YI), and compared. Data was tested through a 10 × 10 repeated k-fold cross validation design. The LR model presented an overall better performance, as assessed by the areas under the receiver operati

SUBMITTER: Albuquerque J 

PROVIDER: S-EPMC9231719 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature