Cloud-Enhanced Personalized Cardiopulmonary Rehabilitation Using Linear Regression

Effective recovery of those with cardiovascular and pulmonary disorders requires cardiopulmonary rehabilitation (CPR). Because patients react and improve differently, traditional CPR programs typically struggle to create individualized therapy strategies. This research presents a novel method for improving CPR customization via integrating cloud computing and linear regression models. Physiological measurements, exercise metrics, and rehabilitation progress are just data sets we gather and analyze using a cloud-based platform. The cloud infrastructure's real-time data synchronization and processing makes rehabilitation program monitoring and modification possible. Using linear regression, modeling and predicting individual patient outcomes using historical data and current inputs. Using this predictive capacity, rehabilitation procedures may be customized to match every patient's unique requirement, maximizing the therapy's efficacy. In addition to facilitating data storage and accessibility, the cloud-based method also makes it easier to use regression models to alter treatments dynamically. Our data show that cloud-enhanced tailored CPR may greatly improve patients' adherence and rehabilitation outcomes. This technique shows great promise for individualized healthcare and rehabilitation tactics by integrating patient data into treatment plans and continually changing them using real-time feedback.