Optimized Tuned Deep Learning Model for Chronic Kidney Disease Classification

In recent times, Internet of Things (IoT) and Cloud Computing (CC) paradigms are commonly employed in different healthcare applications. IoT gadgets generate huge volumes of patient data in healthcare domain, which can be examined on cloud over the available storage and computation resources in mobi...

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Bibliographic Details
Published in:Computers, materials & continua materials & continua, 2022, Vol.70 (2), p.2097-2111
Main Authors: H. Aswathy, R., Suresh, P., Yacin Sikkandar, Mohamed, Abdel-Khalek, S., Alhumyani, Hesham, A. Saeed, Rashid, F. Mansour, Romany
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Classification
Cloud computing
Computer simulation
Data collection
Health care
Internet of Things
Kidney diseases
Machine learning
Mobile computing
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Summary:In recent times, Internet of Things (IoT) and Cloud Computing (CC) paradigms are commonly employed in different healthcare applications. IoT gadgets generate huge volumes of patient data in healthcare domain, which can be examined on cloud over the available storage and computation resources in mobile gadgets. Chronic Kidney Disease (CKD) is one of the deadliest diseases that has high mortality rate across the globe. The current research work presents a novel IoT and cloud-based CKD diagnosis model called Flower Pollination Algorithm (FPA)-based Deep Neural Network (DNN) model abbreviated as FPA-DNN. The steps involved in the presented FPA-DNN model are data collection, preprocessing, Feature Selection (FS), and classification. Primarily, the IoT gadgets are utilized in the collection of a patient’s health information. The proposed FPA-DNN model deploys Oppositional Crow Search (OCS) algorithm for FS, which selects the optimal subset of features from the preprocessed data. The application of FPA helps in tuning the DNN parameters for better classification performance. The simulation analysis of the proposed FPA-DNN model was performed against the benchmark CKD dataset. The results were examined under different aspects. The simulation outcomes established the superior performance of FPA-DNN technique by achieving the highest sensitivity of 98.80%, specificity of 98.66%, accuracy of 98.75%, F-score of 99%, and kappa of 97.33%.
ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2022.019790