PDF(Pubmed)
Abstract：
BACKGROUND: Over the past few decades, diabetes has become a serious public health concern worldwide, particularly in Bangladesh. The advancement of artificial intelligence can be reaped in the prediction of blood glucose levels for better health management. However, the practical validity of machine learning (ML) techniques for predicting health parameters using data from low- and middle-income countries, such as Bangladesh, is very low. Specifically, Bangladesh lacks research using ML techniques to predict blood glucose levels based on basic noninvasive clinical measurements and dietary and sociodemographic information.
OBJECTIVE: To formulate strategies for public health planning and the control of diabetes, this study aimed to develop a personalized ML model that predicts the blood glucose level of urban corporate workers in Bangladesh.
METHODS: Based on the basic noninvasive health checkup test results, dietary information, and sociodemographic characteristics of 271 employees of the Bangladeshi Grameen Bank complex, 5 well-known ML models, namely, linear regression, boosted decision tree regression, neural network, decision forest regression, and Bayesian linear regression, were used to predict blood glucose levels. Continuous blood glucose data were used in this study to train the model, which then used the trained data to predict new blood glucose values.
RESULTS: Boosted decision tree regression demonstrated the greatest predictive performance of all evaluated models (root mean squared error=2.30). This means that, on average, our model\'s predicted blood glucose level deviated from the actual blood glucose level by around 2.30 mg/dL. The mean blood glucose value of the population studied was 128.02 mg/dL (SD 56.92), indicating a borderline result for the majority of the samples (normal value: 140 mg/dL). This suggests that the individuals should be monitoring their blood glucose levels regularly.
CONCLUSIONS: This ML-enabled web application for blood glucose prediction helps individuals to self-monitor their health condition. The application was developed with communities in remote areas of low- and middle-income countries, such as Bangladesh, in mind. These areas typically lack health facilities and have an insufficient number of qualified doctors and nurses. The web-based application is a simple, practical, and effective solution that can be adopted by the community. Use of the web application can save money on medical expenses, time, and health management expenses. The created system also aids in achieving the Sustainable Development Goals, particularly in ensuring that everyone in the community enjoys good health and well-being and lowering total morbidity and mortality.
OBJECTIVE: To formulate strategies for public health planning and the control of diabetes, this study aimed to develop a personalized ML model that predicts the blood glucose level of urban corporate workers in Bangladesh.
METHODS: Based on the basic noninvasive health checkup test results, dietary information, and sociodemographic characteristics of 271 employees of the Bangladeshi Grameen Bank complex, 5 well-known ML models, namely, linear regression, boosted decision tree regression, neural network, decision forest regression, and Bayesian linear regression, were used to predict blood glucose levels. Continuous blood glucose data were used in this study to train the model, which then used the trained data to predict new blood glucose values.
RESULTS: Boosted decision tree regression demonstrated the greatest predictive performance of all evaluated models (root mean squared error=2.30). This means that, on average, our model\'s predicted blood glucose level deviated from the actual blood glucose level by around 2.30 mg/dL. The mean blood glucose value of the population studied was 128.02 mg/dL (SD 56.92), indicating a borderline result for the majority of the samples (normal value: 140 mg/dL). This suggests that the individuals should be monitoring their blood glucose levels regularly.
CONCLUSIONS: This ML-enabled web application for blood glucose prediction helps individuals to self-monitor their health condition. The application was developed with communities in remote areas of low- and middle-income countries, such as Bangladesh, in mind. These areas typically lack health facilities and have an insufficient number of qualified doctors and nurses. The web-based application is a simple, practical, and effective solution that can be adopted by the community. Use of the web application can save money on medical expenses, time, and health management expenses. The created system also aids in achieving the Sustainable Development Goals, particularly in ensuring that everyone in the community enjoys good health and well-being and lowering total morbidity and mortality.
摘要：
背景：在过去的几十年里，糖尿病已成为全球严重的公共卫生问题，特别是在孟加拉国。人工智能的进步可以在血糖水平的预测中收获，以更好地进行健康管理。然而,使用来自低收入和中等收入国家的数据预测健康参数的机器学习（ML）技术的实际有效性，比如孟加拉国，非常低。具体来说,孟加拉国缺乏使用ML技术根据基本的非侵入性临床测量以及饮食和社会人口统计学信息来预测血糖水平的研究。
目的：制定公共卫生规划和糖尿病控制的策略，这项研究旨在开发一种个性化的ML模型，该模型可预测孟加拉国城市企业员工的血糖水平。
方法：基于基本的无创健康检查测试结果，饮食信息，以及孟加拉国格莱en银行综合体271名员工的社会人口统计学特征，5个著名的ML模型，即,线性回归，增强决策树回归，神经网络,决策森林回归，和贝叶斯线性回归，用于预测血糖水平。在这项研究中使用连续的血糖数据来训练模型，然后使用训练的数据来预测新的血糖值。
结果：在所有评估模型中，Boosted决策树回归显示出最大的预测性能（均方根误差=2.30）。这意味着,平均而言,我们模型的预测血糖水平偏离实际血糖水平约2.30mg/dL。研究人群的平均血糖值为128.02mg/dL（SD56.92），指示大多数样品的边界结果(正常值:140mg/dL)。这表明个体应该定期监测他们的血糖水平。
结论：这个支持ML的血糖预测网络应用程序有助于个人自我监测自己的健康状况。该应用程序是在低收入和中等收入国家偏远地区的社区开发的，比如孟加拉国，在心里。这些地区通常缺乏卫生设施，合格的医生和护士数量不足。基于Web的应用程序是一个简单的，实用,以及社区可以采用的有效解决方案。使用Web应用程序可以节省医疗费用，时间,和健康管理费用。创建的系统还有助于实现可持续发展目标，特别是确保社区中的每个人都享有良好的健康和福祉，并降低总发病率和死亡率。
目的：制定公共卫生规划和糖尿病控制的策略，这项研究旨在开发一种个性化的ML模型，该模型可预测孟加拉国城市企业员工的血糖水平。
方法：基于基本的无创健康检查测试结果，饮食信息，以及孟加拉国格莱en银行综合体271名员工的社会人口统计学特征，5个著名的ML模型，即,线性回归，增强决策树回归，神经网络,决策森林回归，和贝叶斯线性回归，用于预测血糖水平。在这项研究中使用连续的血糖数据来训练模型，然后使用训练的数据来预测新的血糖值。
结果：在所有评估模型中，Boosted决策树回归显示出最大的预测性能（均方根误差=2.30）。这意味着,平均而言,我们模型的预测血糖水平偏离实际血糖水平约2.30mg/dL。研究人群的平均血糖值为128.02mg/dL（SD56.92），指示大多数样品的边界结果(正常值:140mg/dL)。这表明个体应该定期监测他们的血糖水平。
结论：这个支持ML的血糖预测网络应用程序有助于个人自我监测自己的健康状况。该应用程序是在低收入和中等收入国家偏远地区的社区开发的，比如孟加拉国，在心里。这些地区通常缺乏卫生设施，合格的医生和护士数量不足。基于Web的应用程序是一个简单的，实用,以及社区可以采用的有效解决方案。使用Web应用程序可以节省医疗费用，时间,和健康管理费用。创建的系统还有助于实现可持续发展目标，特别是确保社区中的每个人都享有良好的健康和福祉，并降低总发病率和死亡率。
