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Journal of IoT in Social, Mobile, Analytics, and Cloud

Big Data Analytics for Improved Risk Management and Customer Segregation in Banking Applications
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Design of Deep Learning Algorithm for IoT Application by Image based Recognition
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Analysis of Serverless Computing Techniques in Cloud Software Framework
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Health Record Management System – A Web-based Application
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A Novel Signal Processing Based Driver Drowsiness Detection System
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IoT Based Monitoring and Control System using Sensors
Volume-3 | Issue-2

Secure Data Sharing Platform for Portable Social Networks with Power Saving Operation
Volume-3 | Issue-3

Review of Internet of Wearable Things and Healthcare based Computational Devices
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Stock Index Prediction with Financial News Sentiments and Technical Indicators
Volume-4 | Issue-3

Hybrid Framework on Automatic Detection and Recognition of Traffic Display board Signs
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Suspicious Human Activity Detection System
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ROBOT ASSISTED SENSING, CONTROL AND MANUFACTURE IN AUTOMOBILE INDUSTRY
Volume-1 | Issue-3

EFFICIENT RESOURCE ALLOCATION AND QOS ENHANCEMENTS OF IOT WITH FOG NETWORK
Volume-1 | Issue-2

Live Streaming Architectures for Video Data - A Review
Volume-2 | Issue-4

IoT Based Monitoring and Control System using Sensors
Volume-3 | Issue-2

Big Data Analytics for Improved Risk Management and Customer Segregation in Banking Applications
Volume-3 | Issue-3

A Novel Signal Processing Based Driver Drowsiness Detection System
Volume-3 | Issue-3

IoT BASED AIR AND SOUND POLLUTION MONITIORING SYSTEM USING MACHINE LEARNING ALGORITHMS
Volume-2 | Issue-1

Analysis of Serverless Computing Techniques in Cloud Software Framework
Volume-3 | Issue-3

Hybrid Intrusion Detection System for Internet of Things (IoT)
Volume-2 | Issue-4

Home / Archives / Volume-5 / Issue-4 / Article-3

Volume - 5 | Issue - 4 | december 2023

Cloud of Things (CoT) based Diabetes Risk Prediction System using BiRNN
B. Vivekanandam 
Pages: 322-339
Cite this article
Vivekanandam, B. (2023). Cloud of Things (CoT) based Diabetes Risk Prediction System using BiRNN. Journal of IoT in Social, Mobile, Analytics, and Cloud, 5(4), 322-339. doi:10.36548/jismac.2023.4.003
Published
18 December, 2023
Abstract

The introduction of Internet of Things (IoT) technology witnesses the continuous and distributed connectivity between different objects and people. Currently, with the emerging technological advances, IoT integrates with the cloud and evolves into a new term called “Cloud of Things” to further enhance human lives. Using predictive analytics and Artificial Intelligence (AI) approaches in the healthcare area allows for the development of more reactive and smart healthcare solutions. As a subfield of AI, the Deep Learning (DL) technique has the potential to analyse the given data accurately, provide valuable insights, and solve complex challenges with its ability to train the model continuously. This study intends to implement a deep learning model – Bidirectional Recurrent Neural Networks (Bi-RNN) to obtain a timely and accurate prediction of diabetes risk without requiring any clinical diagnosis. This method of processing the time series data will highly assist in ensuring preventive care and early disease intervention. The proposed model collects real-time data from IoT devices along with the medical data stored in Electronic Health Records (EHR) to perform predictive analytics. The proposed Bi-RNN based diabetes prediction model results in an accuracy of 97.75%, which is comparatively higher than other existing diabetes risk prediction models.

Keywords

Healthcare Risk Prediction Deep Learning Neural Network Cloud of Things

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