Modeling and Analyzing Hormonal Effects of Depression Based on Petri Nets and Machine Learning

Abstract

Depression has become a common mental illness, and the number of patients has shown an obvious rising trend. However, the exploration of the connection between hormone levels and physical state changes in depression patients is still open. Hormone levels are complex and play a key role in regulating multiple body systems and functions, directly or indirectly influencing overall health and physical state. This work utilizes Petri nets to establish a corresponding model for the transition of hormone levels and states in depression, focusing on the association between different hormone levels and states in depressive patients. At the same time, machine learning methods offer a new approach to predicting the reachability of depression patients’ states. This work enables healthcare professionals to quickly assess patients’ emotional changes and their impact on outcomes, improving resource allocation.