Machine Learning Based Automated Blood Sugar Regulatory System

This study introduces an innovative approach that merges advanced deep learning algorithms to predict an individual's insulin dosage according to their blood glucose levels. The methodology incorporates various techniques, including deep learning regression model ensembles, autoregressive integrated moving average (ARIMA), recurrent neural networks (RNN), Lasso regression, and Long Short-Term Memory (LSTM). The primary objective is to accurately determine the insulin dosage required by diabetes patients to enhance their overall health status and glycemic control. By employing sophisticated modeling strategies and analyzing historical glucose data, the system aims to provide personalized insulin dosage recommendations tailored to each user's physiological response. This research contributes to ongoing endeavors in diabetes management through precision medicine, leveraging cutting-edge technology to enhance patient outcomes.