A Cloud-Based, IoT-Enabled Health Monitoring System Based on Machine Learning Techniques

In health care, the use of Cloud-Based and Internet of Things (IoT) technologies has resulted in more efficient and reliable health monitoring systems. The study presents research on a Cloud-Based, IoT-Enabled Health Monitoring System that uses Machine Learning (ML) methods to evaluate and predict health-related data. Several ML methods, such as K-Nearest Neighbors (KNNs), Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Naive Bayes (NB), are used to classify and interpret health data. A comparative analysis is done to establish their accuracy and precision rate performance. SVM and RF have the highest accuracy, which is 97.53% and 95.76%, while the precision rates are 95.95% and 95.90% in that order, making them the most effective algorithms for the health monitoring system, respectively. This study contributes to Cloud-Based, IoT-enabled healthcare systems by suggesting how ML can be used to create accurate health monitoring and prediction.