Transforming physical healthcare training through integration of machine learning and advanced artificial intelligent methods

Mental healthcare and heart disease continues to be a major cause of death worldwide, making it essential to find effective ways to prevent it. Physical activity has long been known to be important for preventing and treating mental healthcare disease, but it is not always clear how much and what type of activity an individual should do. Artificial intelligence (AI) models that can predict a person's risk of mental healthcare disease based on their individual characteristics have become increasingly popular in recent years. In this study, we used AI models to explore the relationship between physical activity and mental healthcare using data from 100 participants that included information about their demographics, medical history, lifestyle, and environment. Our dataset encompasses crucial variables like age, gender, ethnicity, medical history (including heart disease and high blood pressure), socioeconomic status, physical activity (including type, duration, intensity, and frequency), and environmental factors. In this study we predict the mental healthcare and physical activities effect using machine learning (LR, DT, RF, SVM and KNN) and deep learning (CNN, RNN, TabNet, GAN and DQN) models. We leverage cutting-edge analytics, such as machine learning and deep learning, to forecast mental healthcare history based on these factors and examine physical activities and mental health impact on healthcare. Our proposed machine learning method, Random Forest, has demonstrated remarkable accuracy of 88%, while the deep learning model, TabNet-Transform, has achieved an impressive accuracy of 90%. Our Model play key role in enhancing mental well-being and controlling certain psychoneurological disorders.