International Journal of Science, Engineering and TechnologyAuthors: Somit Kumar Yadav, Rohan Choudhary, Sri Krishna Mishra, Minku Kumar, Subhash Kumar Yadav, Himanshu Kumar, Rahul Kumar shah
Abstract: There are a wide range of variables in the field of health care considered by this AI system such as age, gender, height, weight, body mass index, hours of sleep, hydration techniques, stress levels, physical activities, health symptoms, previous diseases to identify potential healthcare risks that can occur in the future and give personalized recommendations on how to take proper care of your health. Predictive healthcare analysis techniques implemented in this project consist of machine learning algorithms such as Logistic Regression, Decision Tree, Random Forest, and XGBoost for predictive analysis and comparison [7], [8], [24]. Predictive analysis is combined with healthcare rules to establish adaptive health care guidelines related to such areas as food management, performing physical exercises, changing hydration practices, decreasing stress and implementing preventive health care strategies. Such technologies as React (frontend framework), Flask (backend APIs), MySQL (healthcare data management) and Scikit-Learn (implementing machine learning models) are implemented to design this project. Methods such as using JSON Web Token-based authentication help with secure healthcare data processing [20], [27]. This AI system is not intended to be used as diagnostic or treatment tool but as a decision-making health care support system to improve preventive health care and personalize it using AI [2], [12].
DOI: http://doi.org/10.5281/zenodo.20560226
