Fog and Edge Computing-Based Smart Health Care System Using Machine Learning

Data in the medical field include varying degrees of ambiguity and imprecision, which makes it difficult for doctors to improve the health of patients with a group of related suggestive disorders. As a result, doctors use data stored in networks and computing systems to identify a variety of illnesses in patients. The proposed strategy boosts the system’s effectiveness and makes accurate diagnoses and suggestions for treating life-threatening diseases easier. The adaptive prediction systems are assessed, and a machine learning-based model for heart disease detection is trained and compared to the relevant traditional approaches. When compared to other sophisticated classification methods, the cloud-fog-fog architecture was shown to be the most accurate. As a result, the suggested strategy is more accurate in identifying different cardiac issues and suggesting effective treatment options. The performance of the proposed system warrants its application in automated cardiac patient diagnostic and recommendation systems. The Heart Failure Clinical Records repository at UCI provided the dataset for this study, which has 13 characteristics. The experimental findings showed that the CNN model performs better than other deep learning and machine learning models which are proposed by eminent researchers.