An Effective Hybrid Recommender System for Cardiovascular Illness Based on IoT

In this study, machine learning and Internet of Things (IoT) data have been combined to create a hybrid recommender system for managing personalized cardiovascular disease (CVD). This system is implemented in the Jupyter Notebook environment. Data collection, preprocessing, exploratory data analysis, model creation, IoT integration, assessment, privacy safeguards, as well as interoperability were all included in our methodical approach. Findings showed better flexibility, and predictive accuracy, including patient-centered suggestions that addressed crucial facets of CVD therapy. The system's complexity and real-time capabilities will be improved in subsequent work, which promises to have a significant influence on proactive healthcare interventions including patient outcomes. This development is an encouraging step forward in modern healthcare's efforts to prevent and control CVD. The evaluation and assessment stage confirmed the hybrid recommender system's effectiveness and showed how well it could adjust to the specific demands of each patient. Privacy precautions were put in place to ensure that ethical norms and legal requirements were met while addressing concerns about sensitive health information. A primary goal was interoperability, which allowed for a smooth integration with the current health care system.