IoMT ‐Enabled Cloud System for Precision E‐Healthcare and Cardiovascular Disease Prediction

ABSTRACT

The increasing prevalence of cardiovascular diseases (CVD) necessitates advanced predictive and diagnostic methods for personalized e‐healthcare. A deep learning‐based Internet of Medical Things (IoMT) framework that enhances CVD prediction and diagnosis is presented in this study. In the proposed framework, IoMT and cloud computing are integrated to ensure real‐time data collection, remote monitoring, and seamless interoperability between medical devices and healthcare providers. Medical data is analyzed using machine learning algorithms such as support vector machines (SVM), Nave Bayes (NB), and k‐nearest neighbors (KNN). An analysis of the comparison of performance shows that SVM with polynomial kernel and Naive Bayes are both superior when it comes to specific metrics, proving their efficacy in real‐world applications. As a result of the framework's ability to continuously monitor health parameters, early intervention and personalized treatment strategies are possible, greatly contributing to patient health outcomes. Integrating IoMT and DL in healthcare can lead to transformative outcomes.