Predicting non-responders to lifestyle intervention in prediabetes: a machine learning approach.

ABSTRACT:

Background

The clinical care process for people with prediabetes starts with lifestyle intervention, often escalating to more intense treatment due to the low success rate of the first-line intervention. Clinicians lack clear guidelines on which patients would benefit from early treatment with more intensive therapeutic options, so we aimed to develop an algorithm to early identify non-responders to lifestyle intervention for prediabetes.

Method

Several statistical and machine learning algorithms were screened with internal cross-validation on the basis of accuracy and discrimination ability to correctly classify patients that would fail to normalize fasting glycemia within one year of being prescribed a lifestyle intervention, solely based on the first examination measureme

SUBMITTER: Foppiani A 

PROVIDER: S-EPMC11810772 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature