Improving Heart Disease Monitoring Through Intelligent Hybrid Inference Systems

To detect early indications and control heart disease, appropriate monitoring methods are needed. This research introduces clever hybrid inference systems to improve heart disease monitoring. These technologies make precise, timely predictions using machine learning algorithms and expert knowledge, enabling early intervention and personalized treatment strategies. Intelligent hybrid inference systems use patient records, physiological measures, and medical literature to find patterns and insights. Deep learning and ensemble approaches are used to analyze data and identify complex heart disease risk factors. Clinical guidelines, medical protocols, and subject expertise are also integrated. This combination of data-driven algorithms and expert knowledge improves the inference process's interpretability and reliability, allowing healthcare practitioners to make informed predictions. Extensive tests and evaluations on real-world cardiac disease datasets test the proposed approach. Intelligent hybrid inference systems exceed standard methods in accuracy and prediction. The systems also adapt to varied patient populations and data characteristics. This research could change heart disease monitoring and management. Healthcare professionals can improve patient outcomes by using intelligent hybrid inference systems to diagnose cardiac disease early, estimate risk, and customize treatment approaches. This effort improves the lives of heart disease patients by using artificial intelligence and expert knowledge.