Hybrid Machine Learning Model for Heart Disease Prediction: A Review

Heart disease is a leading cause of mortality globally, especially among the elderly and individuals with unhealthy lifestyles. Early detection through regular monitoring and diagnosis is critical in preventing severe outcomes. Traditional hospital data such as X-rays, ECGs, and patient health records are often underutilized. Recent advancements combine Internet of Things (IoT) technologies with hybrid machine learning (ML) techniques to improve heart disease prediction accuracy. This review explores various approaches leveraging IoT sensor data and machine learning algorithms, such as fuzzy logic, random forests, and Q-learning, to enhance cardiac disease detection and monitoring.