Population-centric risk prediction modeling for gestational diabetes mellitus: A machine learning approach.

ABSTRACT:

Aims

The heterogeneity in Gestational Diabetes Mellitus (GDM) risk factors among different populations impose challenges in developing a generic prediction model. This study evaluates the predictive ability of existing UK NICE guidelines for assessing GDM risk in Singaporean women, and used machine learning to develop a non-invasive predictive model.

Methods

Data from 909 pregnancies in Singapore's most deeply phenotyped mother-offspring cohort study, Growing Up in Singapore Towards healthy Outcomes (GUSTO), was used for predictive modeling. We used a CatBoost gradient boosting algorithm, and the Shapley feature attribution framework for model building and interpretation of GDM risk attributes.

Results

UK NICE guidelines showed poor predictability in Singaporean women

SUBMITTER: Kumar M 

PROVIDER: S-EPMC7612635 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature