Predictive modelling of metabolic syndrome in Ghanaian diabetic patients: an ensemble machine learning approach
Emmanuel Acheampong,
Eric Adua,
Christian Obirikorang
et al.
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.
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