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Journal of Diabetes & Metabolic Disorders

Open Access 28-08-2024 | Type 2 Diabetes | Research article

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

Authors: Emmanuel Acheampong, Eric Adua, Christian Obirikorang, Enoch Odame Anto, Emmanuel Peprah-Yamoah, Yaa Obirikorang, Evans Adu Asamoah, Victor Opoku-Yamoah, Michael Nyantakyi, John Taylor, Tonnies Abeku Buckman, Maryam Yakubu, Ebenezer Afrifa-Yamoah

Published in: Journal of Diabetes & Metabolic Disorders

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 regression, k-nearest neighbour, Gaussian Naive Bayes, Gradient boosting classification, and support vector machine.

Results

Distinct metabolic profiles and phenotype clusters were associated with MetS progression. The BORUTA algorithm identified 10 and 16 significant features for pre-MetS and MetS prediction, respectively. For pre-MetS, the top-ranked features were lipid accumulation product (LAP), triglyceride-glucose index adjusted for waist-to-height ratio (TyG-WHtR), coronary risk (CR), visceral adiposity index (VAI) and abdominal volume index (AVI). For MetS prediction, the most influential features were VAI, LAP, waist triglyceride index (WTI), Very low-density cholesterol (VLDLC) and TyG-WHtR. Majority voting ensemble classifier demonstrated superior performance in predicting pre-MetS (AUC = 0.79) and MetS (AUC = 0.87).

Conclusion

Identifying these risk factors reveals the complex interplay between visceral adiposity and metabolic dysregulation in African populations, enabling early detection and treatment. Ethical integration of ML algorithms in clinical decision-making can streamline identification of high-risk individuals, optimize resource allocation, and enable precise, tailored interventions.

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Title: Predictive modelling of metabolic syndrome in Ghanaian diabetic patients: an ensemble machine learning approach
Authors: Emmanuel Acheampong
Eric Adua
Christian Obirikorang
Enoch Odame Anto
Emmanuel Peprah-Yamoah
Yaa Obirikorang
Evans Adu Asamoah
Victor Opoku-Yamoah
Michael Nyantakyi
John Taylor
Tonnies Abeku Buckman
Maryam Yakubu
Ebenezer Afrifa-Yamoah
Publication date: 28-08-2024
Publisher: Springer International Publishing
Keyword: Type 2 Diabetes
Published in: Journal of Diabetes & Metabolic Disorders
Electronic ISSN: 2251-6581
DOI: https://doi.org/10.1007/s40200-024-01491-7

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» View more highlights on the 2024 ESC Congress hub page

A quick guide to ECGs

Electrocardiography
Training Course

Improve your ECG interpretation skills with this comprehensive, rapid, interactive course. Expert advice provides detailed feedback as you work through 50 ECGs covering the most common cardiac presentations to ensure your practice stays up to date.

PD Dr. Carsten W. Israel

Developed by: Springer Medizin

Start the cases

At a glance: The STEP trials

Obesity
Clinical Trial Summary

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine

EASD 2024

Springer Medicine

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

Abstract

Objectives

Methods

Results

Conclusion

2024 ESC Congress

Noise pollution dangerous for heart health

Post-TAVI follow up: deploying virtual assistants

Antihypertensives: more effective at morning or night?

Low Lp(a) levels unlikely to increase the risk of diabetes

» View more highlights on the 2024 ESC Congress hub page

A quick guide to ECGs

At a glance: The STEP trials

EASD 2024

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusion

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2024 ESC Congress

Noise pollution dangerous for heart health

Post-TAVI follow up: deploying virtual assistants

Antihypertensives: more effective at morning or night?

Low Lp(a) levels unlikely to increase the risk of diabetes

» View more highlights on the 2024 ESC Congress hub page

A quick guide to ECGs

At a glance: The STEP trials