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

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.

کلیدواژه ها:

Long-Short-Term-Memory recurrent neural network ، Convolutional neural network ، multivariate time series ، sequential data ، generalized additive mixed models