Open Access

ARTICLE

Optimized Tuned Deep Learning Model for Chronic Kidney Disease Classification

by R. H. Aswathy^1,*, 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:

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

Received 25 April 2021; Accepted 06 June 2021; Issue published 27 September 2021

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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Citations