Explainable artificial intelligence-driven gestational diabetes mellitus prediction using clinical and laboratory markers

Abstract

Gestational diabetes is characterized by hyperglycemia diagnosed during pregnancy. High blood sugar levels are likely to affect both the mother and child. This disease frequently goes undiagnosed due to its fewer prominent symptoms, resulting in severe unmanaged hyperglycemia, obesity, childbirth complications and overt diabetes. Artificial Intelligence is increasingly deployed in the medical field, revolutionizing and automating data processing and decision-making. Machine learning is a subset of artificial intelligence that can create reliable healthcare screening and predictive systems. With the advent of machine learning, detecting gestational diabetes and getting more profound insights about the disease is possible. This study explores the development of a reliable clinical decision support system for gestational diabetes detection using multiple machine learning architectures using combinations of five data balancing methods to detect gestational diabetes. An ensemble stack trained on the synthetic minority oversampling technique with edited nearest neighbor obtained the highest performance with accuracy, sensitivity and precision of 96%, 95% and 99%, respectively. Additionally, a layer of explainable artificial intelligence was added to the best-performing model using libraries such as SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations, Quantum lattice, Explain Like I’m 5 algorithm, Anchor and Feature importance. The importance of factors such as Visceral Adipose Deposit and its contribution toward the prediction of gestational diabetes is explored. This research aims to provide a meaningful and interpretable clinical decision support system to aid healthcare professionals in early gestational diabetes detection and improved patient management.

Keywords:

• Gestational diabetes
• explainable artificial intelligence
• machine learning
• data balancing
• synthetic minority oversampling technique with edited nearest neighbor
• visceral adipose deposit

REVIEWING EDITOR:

• Jenhui Chen, Chang Gung University, Taoyuan, Taiwan

SUBJECTS:

• Artificial Intelligence
• Computation
• Computer Science (General)

Acknowledgements

We would like to thank the Manipal Academy of Higher Education for providing us an opportunity to conduct this research.

Author contributions

V.V.K. performed the experiments and wrote this manuscript. K.C. and N.S. provided the design ideas. S.P. performed model development. R.C.P., D.B. and S.K.S. validated the results.

Disclosure statement

The authors declare no competing interest.

Data availability statement

The dataset is available using the following link. https://www.kaggle.com/datasets/kamyababedi/visceral-adipose.

Open access

Open access funding will be provided by Manipal Academy of Higher Education.