Personalized Compression Therapy Plans using Cloud Computing and ML Analytics

For the treatment of diseases like lymphedema and chronic venous insufficiency, compression therapy is crucial, but conventional methods frequently produce less-than-ideal results because they are impersonal. This paper presents a novel approach that uses cloud computing and decision trees to create personalized compression therapy plans in healthcare settings. The proposed system uses cloud infrastructure's scalability and computational power to handle massive amounts of patient data collected in real-time from wearable Internet of Things (IoT) devices. Through device integration, pertinent physiological parameters can be continuously monitored, allowing prompt therapy plan modifications in response to changing patient requirements. Decision Trees, a machine learning (ML) algorithm selected for its capacity to comprehend complicated datasets and produce useful insights, are essential to the system's operation. Decision trees dynamically customize compression therapy regimens using patient data analysis to maximize patient comfort and treatment effectiveness. Early system trials have produced encouraging outcomes, including notable increases in patient satisfaction and treatment outcomes. This strategy prioritizes the delivery of personalized healthcare, which increases therapeutic efficacy and fosters a patient-centered treatment experience. The combination of cloud computing and Decision Trees is a progressive step forward in the healthcare industry, providing scalable solutions that can be tailored to the specific needs of each patient and method for future developments in personalized medical care.