MyWear revolutionizes real-time health monitoring with comparative analysis of machine learning

This paper facilitates proactive health management, advanced patient care, and early identification of possible health hazards by using MyWear. It is a wearable T-shirt that continuously monitors and predicts physiological parameters such as stress and heart rate fluctuations. In particular, it is especially helpful for managing cardiovascular disease, tracking stress, improving athletic performance, and providing health care. The device was tested with several machine learning models, such as K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, Logistic Regression, Decision Tree, and Stochastic Gradient Descent (SGD) to identify irregular heart rhythms. Using the SVM model, the system detects problems with an average accuracy of 98%. In the future, MyWear-designed as a wearable T-shirt-will seamlessly integrate with mobile applications for real-time data visualization, enhancing patient outcomes and fostering greater user engagement.