The utility of a machine learning model in identifying people at high risk of type 2 diabetes mellitus.

IF 2.7 Q3 ENDOCRINOLOGY & METABOLISM

Expert Review of Endocrinology & Metabolism Pub Date : 2024-09-08 DOI:10.1080/17446651.2024.2400706

Abdullah Alkattan, Abdullah Al-Zeer, Fahad Alsaawi, Alanoud Alyahya, Raghad Alnasser, Raoom Alsarhan, Mona Almusawi, Deemah Alabdulaali, Nagla Mahmoud, Rami Al-Jafar, Faisal Aldayel, Mustafa Hassanein, Alhan Haji, Abdulrahman Alsheikh, Amal Alfaifi, Elfadil Elkagam, Ahmed Alfridi, Amjad Alfaleh, Khaled Alabdulkareem, Nashwa Radwan, Edward W Gregg

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引用次数: 0

Abstract

Background: According to previous reports, very high percentages of individuals in Saudi Arabia are undiagnosed for type 2 diabetes mellitus (T2DM). Despite conducting several screening and awareness campaigns, these efforts lacked full accessibility and consumed extensive human and material resources. Thus, developing machine learning (ML) models could enhance the population-based screening process. The study aims to compare a newly developed ML model's outcomes with the validated American Diabetes Association's (ADA) risk assessment regarding predicting people with high risk for T2DM.

Research design and methods: Patients' age, gender, and risk factors that were obtained from the National Health Information Center's dataset were used to build and train the ML model. To evaluate the developed ML model, an external validation study was conducted in three primary health care centers. A random sample (N = 3400) was selected from the non-diabetic individuals.

Results: The results showed the plotted data of sensitivity/100-specificity represented in the Receiver Operating Characteristic (ROC) curve with an AROC value of 0.803, 95% CI: 0.779-0.826.

Conclusions: The current study reveals a new ML model proposed for population-level classification that can be an adequate tool for identifying those at high risk of T2DM or who already have T2DM but have not been diagnosed.

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机器学习模型在识别 2 型糖尿病高危人群方面的实用性。

背景：根据以往的报告，沙特阿拉伯有很高比例的人未被诊断出患有 2 型糖尿病（T2DM）。尽管开展了多次筛查和宣传活动，但这些工作缺乏全面的可及性，并消耗了大量的人力和物力资源。因此，开发机器学习（ML）模型可以加强基于人群的筛查过程。本研究旨在比较新开发的 ML 模型与经过验证的美国糖尿病协会（ADA）风险评估在预测 T2DM 高危人群方面的结果：研究设计和方法：从国家健康信息中心的数据集中获取患者的年龄、性别和风险因素，用于建立和训练 ML 模型。为了评估所开发的 ML 模型，在三个初级卫生保健中心进行了外部验证研究。从非糖尿病患者中随机抽取样本（N = 3400）：结果显示，灵敏度/100-特异性数据绘制在接收者工作特征曲线（ROC）上，AROC 值为 0.803，95% CI：0.779-0.826：目前的研究揭示了一种新的用于人群分类的 ML 模型，该模型可作为识别 T2DM 高危人群或已患有 T2DM 但尚未确诊的人群的适当工具。

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

Expert Review of Endocrinology & Metabolism ENDOCRINOLOGY & METABOLISM-

CiteScore

4.80

自引率

0.00%

发文量

期刊介绍： Implicated in a plethora of regulatory dysfunctions involving growth and development, metabolism, electrolyte balances and reproduction, endocrine disruption is one of the highest priority research topics in the world. As a result, we are now in a position to better detect, characterize and overcome the damage mediated by adverse interaction with the endocrine system. Expert Review of Endocrinology and Metabolism (ISSN 1744-6651), provides extensive coverage of state-of-the-art research and clinical advancements in the field of endocrine control and metabolism, with a focus on screening, prevention, diagnostics, existing and novel therapeutics, as well as related molecular genetics, pathophysiology and epidemiology.