Cardiac Health Assessment System Using Machine Learning Algorithm

The Cardiac Health Assessment System leverages machine learning algorithms to enhance the early detection and management of cardiovascular diseases (CVDs). By analyzing patient information including medical background, lifestyle factors, and results from diagnostic tests the system can accurately predict the likelihood of cardiac events. Classification models like calculated relapse, choice trees, and Credulous Bayes are used to categorize patients into different risk groups, aiding healthcare providers in making well-informed decisions. The system also offers personalized health recommendations to mitigate identified risks, thereby improving patient outcomes and reducing the burden on healthcare resources. Continuous learning is embedded within the model, enabling it to refine predictions based on new data. This proactive approach aims to shift CVD management from reactive treatment to preventative care, reducing CVD-related mortality and enhancing overall heart health outcomes.