IoT with Cloud-Based End to End Secured Disease Diagnosis Model Using Light Weight Cryptography and Gradient Boosting Tree

Background: With the evolution of the Internet of Things (IoT), technology and its associated devices employed in the medical domain, the different characteristics of online healthcare applications become advantageous for human wellbeing. Aim: The objective of this paper is to present an IoT and cloud-based secure disease diagnosis model. At present, various e-healthcare applications offer online services in diverse dimensions using the Internet of Things (IoT). Methods: In this paper, an efficient IoT and cloud-based secure classification model are proposed for disease diagnosis. People can avail efficient and secure services globally over online healthcare applications through this model. The presented model includes an effective Gradient Boosting Tree (GBT)-based data classification and lightweight cryptographic technique named rectangle. The presented GBT–R model offers a better diagnosis in a secure way. Results: The proposed model was validated using Pima Indians diabetes data and extensive simulation was conducted to prove the consistent results of the employed GBT-R model. Conclusion: The experimental outcome strongly suggested that the presented model shows maximum performance with an accuracy of 94.92. © 2021 Bentham Science Publishers.