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Optimized Tuned Deep Learning Model for Chronic Kidney Disease Classification

R. H. Aswathy1,*, P. Suresh1, Mohamed Yacin Sikkandar2, S. Abdel-Khalek3, Hesham Alhumyani4, Rashid A. Saeed4, Romany F. Mansour5

1 Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, 641407, India
2 Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia
3 Department of Mathematics, College of Science, Taif University, Taif, 21944, Saudi Arabia
4 Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
5 Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, 72511, Egypt

* Corresponding Author: R. H. Aswathy. Email: email

Computers, Materials & Continua 2022, 70(2), 2097-2111. https://doi.org/10.32604/cmc.2022.019790

Abstract

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%.

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APA Style
Aswathy, R.H., Suresh, P., Sikkandar, M.Y., Abdel-Khalek, S., Alhumyani, H. et al. (2022). Optimized tuned deep learning model for chronic kidney disease classification. Computers, Materials & Continua, 70(2), 2097-2111. https://doi.org/10.32604/cmc.2022.019790
Vancouver Style
Aswathy RH, Suresh P, Sikkandar MY, Abdel-Khalek S, Alhumyani H, Saeed RA, et al. Optimized tuned deep learning model for chronic kidney disease classification. Comput Mater Contin. 2022;70(2):2097-2111 https://doi.org/10.32604/cmc.2022.019790
IEEE Style
R.H. Aswathy et al., “Optimized Tuned Deep Learning Model for Chronic Kidney Disease Classification,” Comput. Mater. Contin., vol. 70, no. 2, pp. 2097-2111, 2022. https://doi.org/10.32604/cmc.2022.019790

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cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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