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Prediction of Chronic Kidney Disease Based on Cloud with IoT model in Smart Cities using DL/ML Model

Authors:
B. Saroja, G. Rajesh, Thirumalraj Karthikeyan, S. Venkatasubramanian, Kawsher Rahman

Addresses:
Department of Electronics and Communication Engineering, Siddartha Institute of Science and Technology, Puttur, Andhra Pradesh, India. Department of Electronics and Communication Engineering, New Horizon College of Engineering, Bengaluru, Karnataka, India. Department of Artificial Intelligence, Trichy Research Labs, Quest Technologies, Tiruchirappalli, Tamil Nadu, India. Department of Computer Science and Business Systems, Saranathan College of Engineering, Tiruchirappalli, Tamil Nadu, India. Department of General Medicine, Beanibazar Cancer and General Hospital, Dashgram, Sylhet, Bangladesh.

Abstract:

Cloud computing and the IoT have been more prevalent in numerous healthcare applications in recent years due to their ability to integrate monitoring items like sensors and medical gadgets for investigating distant patients. Instead of relying on limited dispensation and storage resources, the massive amounts of data generated by therapeutic IoT strategies may be analysed in a CC context, leading to improved healthcare delivery. The death rate from CKD can be drastically decreased if patients are diagnosed with the condition early. This research proposes a strategy for predicting CKD status using diagnostic medical data from the UCI repository, which involves preprocessing, handling missing values, aggregating, extracting, and classifying the data, and generating predictions. The suggested method utilises medical data from IoT devices and benchmark repositories to detect and classify multiple stages of CKD. Furthermore, the suggested method employs the Adaptive Synthetic (ADASYN) method for outlier identification. This research offers a deep (GRU) network model for CKD classification to label the CKD category accurately. 

Keywords: Internet of Things; Chronic Kidney Disease; Adaptive Synthetic Technique; Bidirectional Memory; Gated Recurrent Units; Red Colobus Monkey; Electronic Health Records.

Received on: 12/11/2024, Revised on: 21/01/2025, Accepted on: 13/03/2025, Published on: 07/09/2025

DOI: 10.69888/FTSHSL.2025.000500

FMDB Transactions on Sustainable Health Science Letters, 2025 Vol. 3 No. 3, Pages: 135-147

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