Machine Learning for Reclassification of Obesity

Data-driven Clustering for Metabolic Classification of Obesity Using Machine Learning

The goal of this study is to employ or develop computational modeling techniques for the precise reclassification of obesity into subgroups. Clinical features, risks of noncommunicable diseases, as well as weight loss effects of bariatric surgery will also be studied and compared within the subgroups.

Inclusion Criteria:

1. Patients with overweight/obesity
2. Patients with normal weight as controls

Exclusion Criteria:

1. had ever been performed with a bariatric surgery before the study's first visit is scheduled;
2. had taken exogenous insulin, medication that affects glucose metabolism, or uric acid drugs currently;
3. being diagnosed with type 1 diabetes, secondary diabetes, hereditary disease, or severe disease (e.g. malignant tumor, heart failure, liver failure, etc.);
4. in gestation of lactation;
5. did not have the complete data for model;
6. for normal-weight controls, patients with diabetes or hyperuricemia were excluded.