Predictive modelling of metabolic syndrome in Ghanaian diabetic patients: an ensemble machine learning approach.

ABSTRACT:

Objectives

The burgeoning prevalence of cardiometabolic disorders, including type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS) within Africa is concerning. Machine learning (ML) techniques offer a unique opportunity to leverage data-driven insights and construct predictive models for MetS risk, thereby enhancing the implementation of personalised prevention strategies. In this work, we employed ML techniques to develop predictive models for pre-MetS and MetS among diabetic patients.

Methods

This multi-centre cross-sectional study comprised of 919 T2DM patients. Age, gender, novel anthropometric indices along with biochemical measures were analysed using BORUTA feature selection and an ensemble majority voting classification model, which included logistic regressi

SUBMITTER: Acheampong E 

PROVIDER: S-EPMC11599523 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature