The paper introduces FH-TabNet, a multi-stage tabular deep learning network for detecting Familial Hypercholesterolemia (FH) in multiple classes. FH is a genetic disorder characterized by high LDL cholesterol levels, posing cardiovascular risks. Existing diagnosis methods are complex and underdiagnose cases. FH-TabNet uses TabNet architecture to categorize patients into Definite, Probable, Possible, and Unlikely classes. The model shows superior performance in categorizing low-prevalence subcategories of FH patients through 5-fold cross-validation.
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arxiv.org
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