Prediction of blood glucose level of type 1 diabetics using response surface methodology and data mining
Prediction of blood glucose level of type 1 diabetics using response surface methodology and data mining
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DOI：
10.1007/s11517-006-0049-x
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发表时间：
2006-06-01
影响因子：
3.2
通讯作者：
Kobayashi, M.
中科院分区：
文献类型：
--
作者：
Yamaguchi, M.;Kaseda, C.;Kobayashi, M.
为了提高预测血糖水平的准确性，有必要获得生活方式的细节，并优化依赖于糖尿病患者的输入变量。本研究选取4名1型糖尿病患者，记录其5个月以上的空腹血糖水平（FBG）、代谢率、食物摄入量和身体状况作为初步研究。然后，利用数据挖掘的方法，得到空腹血糖的估计模型，进而预测次日早晨血糖水平的波动趋势。以正(1)至负(5)的量表对被试的身体状况进行自我评估，并将其数值设置为身体状况变量。通过在输入变量中加入物理条件变量进行数据挖掘，提高了光纤光栅预测的精度。为了从反映受试者葡萄糖代谢的生物信息中确定更合适的输入变量，采用响应面法（response surface methodology， RSM）。因此，使用与RSM中FBG显示正相关的变量，FBG预测的准确性得到了提高。可以找到这样的条件，对血糖水平波动趋势的预测准确率达到80%左右。对次日早晨血糖波动趋势的预测方法，可能有助于1型糖尿病患者通过胰岛素治疗提高生活质量，预防低血糖的发生。
In order to improve the accuracy of predicting blood glucose levels, it is necessary to obtain details about the lifestyle and to optimize the input variables dependent on diabetics. In this study, using four subjects who are type 1 diabetics, the fasting blood glucose level (FBG), metabolic rate, food intake, and physical condition are recorded for more than 5 months as a preliminary study. Then, using data mining, an estimation model of FBG is obtained, and subsequently, the trend in fluctuations in the next morning's glucose level is predicted. The subject's physical condition is self-assessed on a scale from positive (1) to negative (5), and the values are set as the physical condition variable. By adding the physical condition variable to the input variables for the data mining, the accuracy of the FBG prediction is improved. In order to determine more appropriate input variables from the biological information reflecting on the subject's glucose metabolism, response surface methodology (RSM) is employed. As a result, using the variables exhibiting positive correlations with the FBG in the RSM, the accuracy of the FBG prediction improved. Conditions could be found such that the accuracy of the predicting trends in fluctuations in blood glucose level reached around 80%. The prediction method of the trend in fluctuations in the next morning's glucose levels might be useful to improve the quality of life of type 1 diabetics through insulin treatment, and to prevent hypoglycemia.