Data Analysis for Developing Blood Glucose Level Control System

Risa Tamaki, Manato Fujimoto, H. Suwa, K. Yasumoto
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Abstract

Since approximately 10% of people have now diabetes in Japan, the importance of diabetes prevention is increasing. Recently, there are many support programs which allow a person with diabetes to control blood glucose level. However, there are few ways to help non-diabetic people avoid becoming diabetics. Too high peak blood glucose level and prolonged postprandial hyperglycemia can lead to lifestyle-related diseases such as type 2 diabetes. Therefore, it is important to prevent before patients get these diseases. For this purpose, blood glucose level control is required. In this paper, we propose a system for non-diabetic persons to control blood glucose level by predicting it before eating a meal from its image captured. Specifically, we recommend not eating a meal that causes a significant increase in blood glucose level. We analyzed data to create and validate a blood glucose estimation model as the first step toward the realization of a blood glucose level control system. We collected and characterized data on Glycemic Index(GI) of the meal, the time elapsed since the last meal, and the bedtime and sleeping time from four participants to construct a blood glucose level estimation model for each participant using Random Forest.As a result, the constructed estimation models for four participants could estimate blood glucose level with RMSE of 15.41, 12.84, 10, and 10.09, R2 of 0.21, 0.54, 0.75, and 0.82, and finally, MAE of 11.64, 9.232, 6.44, and 6.00.
血糖水平控制系统的数据分析
由于目前日本约有10%的人患有糖尿病,因此预防糖尿病的重要性正在增加。最近,有许多支持计划允许糖尿病患者控制血糖水平。然而,有一些方法可以帮助非糖尿病患者避免成为糖尿病患者。过高的血糖峰值和持续的餐后高血糖会导致与生活方式相关的疾病,如2型糖尿病。因此,在患者患上这些疾病之前进行预防是很重要的。为此,需要控制血糖水平。在本文中,我们提出了一个系统,为非糖尿病患者控制血糖水平通过预测饭前从其图像捕获。具体来说,我们建议不要吃一顿会导致血糖水平显著升高的饭。我们分析数据来创建和验证血糖估计模型,作为实现血糖水平控制系统的第一步。我们收集了四名参与者的血糖指数(GI)、距离最后一餐的时间、就寝时间和睡眠时间等数据并进行了特征化,利用随机森林构建了每个参与者的血糖水平估计模型。结果表明,所构建的4名受试者的血糖水平估计模型的RMSE分别为15.41、12.84、10和10.09,R2分别为0.21、0.54、0.75和0.82,MAE分别为11.64、9.232、6.44和6.00。
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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