IoT-Cloud-Based Smart Healthcare Monitoring System for Heart Disease Prediction via Deep Learning

TLDR

The Internet of Things, combined with cloud computing, enables seamless connectivity that, together with predictive analytics and deep learning, can transform reactive healthcare into proactive disease prevention and early intervention. The study aims to develop highly accurate heart disease prediction models by leveraging recurrent neural network variants of deep learning to handle sequential electronic clinical records. The system collects IoT sensor data and cloud‑stored electronic clinical records, then applies recurrent neural network analytics to predict heart disease risk. The Bi‑LSTM‑based system achieved 98.86 % accuracy, 98.9 % precision, 98.8 % sensitivity, 98.89 % specificity, and 98.86 % F‑measure, outperforming existing smart heart disease prediction systems.

Abstract

The Internet of Things confers seamless connectivity between people and objects, and its confluence with the Cloud improves our lives. Predictive analytics in the medical domain can help turn a reactive healthcare strategy into a proactive one, with advanced artificial intelligence and machine learning approaches permeating the healthcare industry. As the subfield of ML, deep learning possesses the transformative potential for accurately analysing vast data at exceptional speeds, eliciting intelligent insights, and efficiently solving intricate issues. The accurate and timely prediction of diseases is crucial in ensuring preventive care alongside early intervention for people at risk. With the widespread adoption of electronic clinical records, creating prediction models with enhanced accuracy is key to harnessing recurrent neural network variants of deep learning possessing the ability to manage sequential time-series data. The proposed system acquires data from IoT devices, and the electronic clinical data stored on the cloud pertaining to patient history are subjected to predictive analytics. The smart healthcare system for monitoring and accurately predicting heart disease risk built around Bi-LSTM (bidirectional long short-term memory) showcases an accuracy of 98.86%, a precision of 98.9%, a sensitivity of 98.8%, a specificity of 98.89%, and an F-measure of 98.86%, which are much better than the existing smart heart disease prediction systems.