A Secure IoT-Cloud Based Remote Health Monitoring for Heart Disease Prediction using machine learning and deep learning techniques

Context:

The Internet of Things (IoT) refers to a network of interconnected devices as well as technology that enables objects to communicate with one another with the cloud for modern medical treatment. New technologies, such as context-aware systems and apps, are continually being introduced into the field of medicine. This work creates an IoT-enabled healthcare system based on context awareness.

Objectives:

Deep learning is the subset of Machine Learning, which has the transformative ability to rapidly analyze massive amounts of data, produce insightful conclusions and effectively resolves complex problems. The objective of this work is to employ an IoT framework for heart disease prediction.

Method:

An RHMIoT framework is proposed in a secure IoT and cloud context using a lightweight block encryption and decryption approach. Using IoT medical sensors patient clinical data are gathered to classify the severity of hypertension, hypercholesterolemia and heart disease. The accuracy levels of cardiac disease are calculated using Deep Learning and auto-encoder-based methods.

Findings: We present a novel strategy for identifying key features using machine learning and deep learning techniques in a secured cloud environment to improve the accuracy of CVD.

Conclusion:

A lightweight block encryption and decryption technique is provided for a secure RHMIoT. The outcomes were determined using several performance matrices. The performance of auto-encoder Kernel SVM model provided the greatest accuracy of 87.00%. The suggested remote health monitoring system identifies the presence of heart disease in a patient and helps to get quick medical attention in case of an emergency situation.