A Comprehensive Review of Heart Disease Prediction using Cloud-Driven Machine Learning

According to a recent report by the World Health Organisation (WHO), a third of the world's population will die as a result of cardiovascular disorders, which include coronary heart disease, heart attacks, and vascular disease. The most commonly used traditional techniques are unreliable, inaccurate, and time-consuming. Early prediction of unhealthy heart functions by specialists can reduce the mortality rate. However, achieving dependable identification of coronary heart issues in all situations and providing 24-hour patient consultation by a doctor is still not feasible. To address these challenges, advanced machine learning techniques combined with a cloud-based Internet of Things (IoT) environment have been implemented. A cloud-based IoT platform assists in monitoring patients' health using machine learning algorithms to predict their health status accurately. In this study, a comparison of various machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), k-Nearest Neighbours (KNN), Random Forest (RF), and Gradient Boosting (GB) has been conducted. Identifying the most effective machine learning algorithm for the early detection and precise prediction of heart failure is crucial, especially with the emergence of cloud computing trends. This is essential for reducing mortality rates and providing optimal guidance in clinical decisions for treating cardiac patients. To achieve accurate diagnosis of cardiovascular disease, this research study provides an overview of current research developments, challenges, and the potential for further research.