Machine Learning for Reclassification of Obesity

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

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT04282837

Enrollment

2495

Registered

2020-02-25

Start date

2020-03-01

Completion date

2020-06-20

Last updated

2020-06-25

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Obesity

Keywords

Obesity, Machine learning, Metabolic classification, Clustering, Bariatric surgery

Brief summary

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.

Interventions

DIAGNOSTIC_TESTAI classification of patients with obesity

Computational modeling techniques will be used for the precise reclassification of obesity into four subgroups, several variables according to the clinical experience and the modeling results will be selected for the cluster analysis.

Study design

Observational model

CASE_CONTROL

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

10 Years to 70 Years

Healthy volunteers

Yes

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.

Design outcomes

Primary

Measure	Time frame
Metabolic classification of patients with obesity using machine learning	baseline

Secondary

Measure	Time frame
Metabolic features in patients of different subgroups	baseline
Risks for noncommunicable disease in patients of different subgroups	baseline
Effect of bariatric surgery in patients of different subgroups	1 year after bariatric surgery

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 14, 2026