使用经典机器学习模型进行基于语音的糖尿病前期预测
Jessica Oreskovic1, Ghazal Fazli2, Vanita Varma3
1Klick Applied Sciences, Klick, Inc., Toronto, ON, Canada.
Frontiers in clinical diabetes and healthcare
|December 15, 2025
概括
语音分析显示了查糖尿病前期的潜力,但模型难以在不同人群中概括. 需要对各种数据进行进一步的研究,以便在现实世界中应用.
科学领域:
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
- 代谢性疾病研究研究
背景情况:
- 糖尿病前期是一种常见的疾病,增加了2型糖尿病和心血管疾病的风险.
- 超过80%的糖尿病前期患者仍未被诊断出来,这突显了公共卫生领域的严重差距.
- 语音分析提供了一种非侵入性查方法,先前成功检测高血压和2型糖尿病.
研究的目的:
- 研究基于语音的机器学习模型在识别糖尿病前期患者的有效性.
- 评估这些基于语音的模型在不同人群中的通用性.
主要方法:
- 来自印度和加拿大的参与者通过移动应用程序提供语音录音;通过HbA1c评估血糖状况.
- 从语音样本中提取了167个声学特征,并开发了性别特定的机器学习模型.
- 模型使用L1-规范化后勤回归 (LASSO) 进行特征选择和SMOTE用于类失衡,并通过交叉验证和持久测试进行评估.
主要成果:
- 在交叉验证中,最佳女性模型获得了0.78平衡精度,最佳男性模型获得了0.68.
- 坚持测试显示,在不平衡的数据集上训练的男性XGBoost模型比交叉验证模型更好地概括.
- 模型在独立的加拿大数据集上表现出不良的概括性,其中一些无法准确识别糖尿病前参与者.
结论:
- 基于语音的模型在受控环境中显示出预糖尿病查的前景.
- 当在不同的地理或人口群体中应用时,模型性能显著下降.
- 开发更强大,更适用的选工具需要多样化的培训数据和特定人群的模型调整.
更多相关视频
06:22Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
Published on: September 19, 2025
399
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
8.6K
相关概念视频
01:03Prediction Intervals
3.1K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
3.1K
01:17Classification of Illness
8.5K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
8.5K
01:30Classification of Signals
1.3K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.3K