Publication

Abstract : Fast food consumption and changes in lifestyle are associated with an increase in heart related problems. This study looks at how cloud computing, the Internet of Things and machine learning can be combined for CVD risk prediction and real-time monitoring. The IoT-ML-Cloud paradigm aims to increment the precision of Cardio Vascular Disease prediction by merging these technologies. The system comprises three main modules: Prediction, Data collection and User engagement. Utilizing Arduino and ESP32 modules. An Android application facilitates patient data input and real-time monitoring and predictions using ML models deployed on the Microsoft Azure cloud. Bluetooth technology utilised to facilitates real-time data transfer to the cloud. This paper proposes Bi-LSTM algorithm with an accuracy of 96.55% for the heart disease prediction. Along with highlighting significant discoveries from numerous ML algorithms in the diagnosis of Cardio Vascular Disease, it also highlights the significance of cloud computing and data mining for large-scale medical data analysis.