Predicting the future risk of developing type 2 diabetes in women with a history of gestational diabetes mellitus using machine learning and explainable artificial intelligence

IF 2.3 4区医学 Q3 ENDOCRINOLOGY & METABOLISM

Primary Care Diabetes Pub Date : 2025-12-01 Epub Date: 2025-09-25 DOI:10.1016/j.pcd.2025.09.006

Jenifar Prashanthan , Amirthanathan Prashanthan

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引用次数: 0

Abstract

Background and aim

It is essential to identify the risk of developing Type 2 Diabetes Mellitus (T2DM) in women with a history of Gestational Diabetes Mellitus (GDM). This study seeks to create a machine learning (ML) model combined with explainable artificial intelligence (XAI) to predict and explain the risk of Type 2 Diabetes Mellitus (T2DM) in women with a history of Gestational Diabetes Mellitus (GDM).

Methods

A literature review found 28 risk factors, including pregnancy-related clinical risk factors, maternal characteristics, genetic risk factors, and lifestyle and modifiable risk factors. A synthetic dataset was generated utilizing subject expertise and clinical experience through Python programming. Various machine learning classification techniques were employed on the data to identify the optimal model, which integrates interpretability approaches (SHAP) to guarantee the transparency of model predictions.

Results

The developed machine learning model exhibited superior accuracy in predicting the risk of T2DM relative to conventional clinical risk scores, with notable contributions from factors such as insulin treatment during pregnancy, physical inactivity, obesity, breastfeeding, a history of recurrent GDM, an unhealthy diet, and ethnicity. Integrated XAI assists clinicians in comprehending the relevant risk factors and their influence on certain predictive outcomes.

Conclusions

Machine learning and explainable artificial intelligence provide a comprehensive methodology for individualized risk evaluation in women with a history of gestational diabetes mellitus. This methodology, by integrating extensive real-world data, offers healthcare clinicians actionable insights for early intervention.

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使用机器学习和可解释的人工智能预测有妊娠糖尿病史的妇女未来发展为2型糖尿病的风险

背景和目的：有妊娠期糖尿病（GDM）病史的妇女发生2型糖尿病（T2DM）的风险至关重要。本研究旨在创建一个结合可解释人工智能（XAI）的机器学习（ML）模型，以预测和解释有妊娠糖尿病（GDM）史的女性患2型糖尿病（T2DM）的风险。方法：通过文献回顾，发现28个危险因素，包括妊娠相关临床危险因素、孕产妇特征、遗传危险因素、生活方式及可改变的危险因素。通过Python编程，利用学科专业知识和临床经验生成合成数据集。采用多种机器学习分类技术对数据进行分类，并结合可解释性方法（SHAP）来确定最优模型，保证模型预测的透明性。结果：与传统临床风险评分相比，开发的机器学习模型在预测T2DM风险方面表现出更高的准确性，其中包括怀孕期间胰岛素治疗、缺乏运动、肥胖、母乳喂养、复发性GDM史、不健康饮食和种族等因素的显著贡献。集成的XAI帮助临床医生理解相关的风险因素及其对某些预测结果的影响。结论：机器学习和可解释的人工智能为妊娠期糖尿病患者的个体化风险评估提供了一种全面的方法。该方法通过整合广泛的真实世界数据，为早期干预提供了医疗保健临床医生可操作的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Primary Care Diabetes ENDOCRINOLOGY & METABOLISM-PRIMARY HEALTH CARE

CiteScore

5.00

自引率

3.40%

发文量

134

审稿时长

47 days

期刊介绍： The journal publishes original research articles and high quality reviews in the fields of clinical care, diabetes education, nutrition, health services, psychosocial research and epidemiology and other areas as far as is relevant for diabetology in a primary-care setting. The purpose of the journal is to encourage interdisciplinary research and discussion between all those who are involved in primary diabetes care on an international level. The Journal also publishes news and articles concerning the policies and activities of Primary Care Diabetes Europe and reflects the society''s aim of improving the care for people with diabetes mellitus within the primary-care setting.