Detecting Type 2 Diabetes (T2D) and Prediabetes (PD) is a real challenge for
medicine due to the absence of pathogenic symptoms and the lack of known
associated risk factors. Even though some proposals for machine learning models
enable the identification of people at risk, the nature of the condition makes
it so that a model suitable for one population may not necessarily be suitable
for another. In this article, the development and assessment of predictive
models to identify people at risk for T2D and PD specifically in Argentina are
discussed. First, the database was thoroughly preprocessed and three specific
datasets were generated considering a compromise between the number of records
and the amount of available variables. After applying 5 different
classification models, the results obtained show that a very good performance
was observed for two datasets with some of these models. In particular, RF, DT,
and ANN demonstrated great classification power, with good values for the
metrics under consideration. Given the lack of this type of tool in Argentina,
this work represents the first step towards the development of more
sophisticated models.

利用机器学习模型为阿根廷地区特定生成 T2D 和 PD 风险人群预测模型的发展和评估，证明了 RF、DT 和 ANN 方法在分类能力方面表现出色，为阿根廷地区的更复杂模型开发迈出了第一步。

利用机器学习技术在阿根廷对糖尿病风险人群的首次识别体验

First Experiences with the Identification of People at Risk for Diabetes  in Argentina using Machine Learning Techniques

This document provides responses to the FDA's request for public comments
(Docket No FDA 2023 N 4853) on the role of digital health technologies (DHTs)
in detecting prediabetes and undiagnosed type 2 diabetes. It explores current
DHT applications in prevention, detection, treatment and reversal of
prediabetes, highlighting AI chatbots, online forums, wearables and mobile
apps. The methods employed by DHTs to capture health signals like glucose,
diet, symptoms and community insights are outlined. Key subpopulations that
could benefit most from remote screening tools include rural residents,
minority groups, high-risk individuals and those with limited healthcare
access. Capturable high-impact risk factors encompass glycemic variability,
cardiovascular parameters, respiratory health, blood biomarkers and patient
reported symptoms. An array of non-invasive monitoring tools are discussed,
although further research into their accuracy for diverse groups is warranted.
Extensive health datasets providing immense opportunities for AI and ML based
risk modeling are presented. Promising techniques leveraging EHRs, imaging,
wearables and surveys to enhance screening through AI and ML algorithms are
showcased. Analysis of social media and streaming data further allows disease
prediction across populations. Ongoing innovation focused on inclusivity and
accessibility is highlighted as pivotal in unlocking DHTs potential for
transforming prediabetes and diabetes prevention and care.

该研究总结了数字健康技术在预防、检测和治疗潜在糖尿病和未经确诊的 2 型糖尿病方面的应用，重点介绍了 AI 聊天机器人、在线论坛、可穿戴设备和移动应用。此外，还探讨了通过 AI 和机器学习算法提高筛查效果的技术以及通过分析社交媒体和流媒体数据进行人群疾病预测的方法。