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Amini, Morteza, Ghasemifard, Kiana. (1402). Blood glucose range, direction, and abnormal value prediction based on regular and NPH insulin doses using machine learning and statistical methods. سامانه مدیریت نشریات علمی, 2(1), 115-131. doi: 10.22054/jdsm.2024.79363.1046
Morteza Amini; Kiana Ghasemifard. "Blood glucose range, direction, and abnormal value prediction based on regular and NPH insulin doses using machine learning and statistical methods". سامانه مدیریت نشریات علمی, 2, 1, 1402, 115-131. doi: 10.22054/jdsm.2024.79363.1046
Amini, Morteza, Ghasemifard, Kiana. (1402). 'Blood glucose range, direction, and abnormal value prediction based on regular and NPH insulin doses using machine learning and statistical methods', سامانه مدیریت نشریات علمی, 2(1), pp. 115-131. doi: 10.22054/jdsm.2024.79363.1046
Amini, Morteza, Ghasemifard, Kiana. Blood glucose range, direction, and abnormal value prediction based on regular and NPH insulin doses using machine learning and statistical methods. سامانه مدیریت نشریات علمی, 1402; 2(1): 115-131. doi: 10.22054/jdsm.2024.79363.1046

Blood glucose range, direction, and abnormal value prediction based on regular and NPH insulin doses using machine learning and statistical methods

Journal of Data Science and Modeling
دوره 2، شماره 1 - شماره پیاپی 3، اسفند 2023، صفحه 115-131    اصل مقاله (7.16 M)
نوع مقاله: original
شناسه دیجیتال (DOI): 10.22054/jdsm.2024.79363.1046
نویسندگان
Morteza Amini* 1؛ Kiana Ghasemifard2
1Department of Statistics, School of Mathematics, Statistics Aand Computer Science, College of Science, University of Tehran
2Dpt. of Statistics, School of Mathematics, Statistics and Computer Science, University of Tehran, Tehran, Iran
چکیده
The diabetes data set gathered by Michael Kahn, at Washington University, St. Louis, MO, which is available online at UCI machine learning repository is one of the rarely used data sets, specially for glucose prediction purposes in diabetic patients. In this paper, we study the problem of blood glucose range prediction, rather than raw glucose prediction, along with two other important tasks, which are the detection of increment or decrement of glucose as well as abnormal value prediction, based on regular and NPH insulin doses, based on this data set. Two commonly used machine learning approaches for time series data, namely LSTM and CNN are used along with a promising statistical regression approach, that is non-parametric multivariate Gaussian additive mixed model, for the prediction task. It is observed that, although LSTM and CNN models are preferable concerning the prediction error, the statistical method performs significantly better in the sense of abnormal value detection, which is a critical task for diabetic patients.
کلیدواژه‌ها
Long-Short-Term-Memory recurrent neural network؛ Convolutional neural network؛ multivariate time series؛ sequential data؛ generalized additive mixed models
آمار
تعداد مشاهده مقاله: 325
تعداد دریافت فایل اصل مقاله: 283


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