부산대학교 도서관

One of the common diseases that many individuals experience is diabetes. One way to think of diabetes is as a collection of physiological a condition where the body's blood glucose level is higher than the standard guidelines for treatment. Typical method to identify the blood samples are drawn to measure blood glucose levels, which is highly painful. The creation of a blood glucose prediction model is the solution for preventing diabetes and may regulate insulin levels. Here, we suggest a novel deep learning forecasting model for the precise diabetes classification by blood glucose level prediction. The proposed model makes use of long short-term memory (LSTM) in identifying both short- and long-term dependencies, as well as convolutional layers to extract meaningful knowledge and learn the internal representation of time-series data. In this research work proposed CNN-LSTM hybrid model's performance is tested against the most advanced deep learning and machine learning models currently in use. According to the preliminary experimental research, using LSTM layers in conjunction with extra convolutional layers could significantly improve forecasting ability.