FH-TabNet: Multi-Class Familial Hypercholesterolemia Detection via a
  Multi-Stage Tabular Deep Learning
Sadaf Khademi Zohreh Hajiakhondi Golnaz Vaseghi Nizal Sarrafzadegan Arash Mohammadi
Familial Hypercholesterolemia (FH) is a genetic disorder characterized by
elevated levels of Low-Density Lipoprotein (LDL) cholesterol or its associated
genes. Early-stage and accurate categorization of FH is of significance
allowing for timely interventions to mitigate the risk of life-threatening
conditions. Conventional diagnosis approach, however, is complex, costly, and a
challenging interpretation task even for experienced clinicians resulting in
high underdiagnosis rates. Although there has been a recent surge of interest
in using Machine Learning (ML) models for early FH detection, existing
solutions only consider a binary classification task solely using classical ML
models. Despite its significance, application of Deep Learning (DL) for FH
detection is in its infancy, possibly, due to categorical nature of the
underlying clinical data. The paper addresses this gap by introducing the
FH-TabNet, which is a multi-stage tabular DL network for multi-class (Definite,
Probable, Possible, and Unlikely) FH detection. The FH-TabNet initially
involves applying a deep tabular data learning architecture (TabNet) for
primary categorization into healthy (Possible/Unlikely) and patient
(Probable/Definite) classes. Subsequently, independent TabNet classifiers are
applied to each subgroup, enabling refined classification. The model's
performance is evaluated through 5-fold cross-validation illustrating superior
performance in categorizing FH patients, particularly in the challenging
low-prevalence subcategories.