Intelligent Health Promotion: Machine Learning in the Prevention of Stress-Related Diseases

Diseases caused by slow and progressive damage are the leading cause of mortality. The stress experienced throughout the day can cause many illnesses, as it is responsible for diminishing the body's defenses. In such cases, prevention is a fundamental component achieved through monitoring individuals, usually through a process that heavily depends on human intervention. Therefore, developing solutions capable of automated monitoring becomes necessary to assist individuals in their daily lives. This work aims to promote individuals' health by monitoring them through smart wearable devices and providing notifications that enable them to learn more about themselves. The work focuses on developing a computational environment composed of wearable devices and an application integrated with a machine-learning model. This model predicts the user's heart rate data and generates notifications accordingly. The results show that real-time user monitoring is possible, and moments of stress can be identified using machine learning, leading to generating notifications.