Optimizing hypoglycemia prediction models by combining genetic and Levenberg-Marquardt algorithms

This paper investigates a method for anticipating hypoglycemic episodes in individuals with type 1 diabetes using artificial neural networks (ANNs). Hypoglycemia is a significant problem that often occurs as a result of intensive insulin treatment; nocturnal episodes are particularly dangerous because symptoms can often be misinterpreted or ignored during sleep and can progress to seizures, coma, or even death. The study examines continuous glucose monitoring (CGM) data over a 3-month period, insulin dose records, and food diaries of five individuals diagnosed with type 1 diabetes. By analyzing available historical data, the proposed method trains a deep learning model to detect patterns that can signal the risk of hypoglycemia in advance. Additionally, the research presents an approach to improve the sensitivity and specificity of predictions by incorporating genetic algorithms and Levenberg-Marquardt in the training process. This adaptation refines the predictive accuracy of the model and enhances dynamic adaptation to the unique characteristics of each individual patient to improve the speed and reliability of its predictions.