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Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease

At present times, healthcare systems are updated with advanced capabilities like machine learning (ML), data mining and artificial intelligence to offer human with more intelligent and expert healthcare services. This paper introduces an intelligent prediction and classification system for healthcar...

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Detalles Bibliográficos
Autores principales: Elhoseny, Mohamed, Shankar, K., Uthayakumar, J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6610122/
https://www.ncbi.nlm.nih.gov/pubmed/31270387
http://dx.doi.org/10.1038/s41598-019-46074-2
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author Elhoseny, Mohamed
Shankar, K.
Uthayakumar, J.
author_facet Elhoseny, Mohamed
Shankar, K.
Uthayakumar, J.
author_sort Elhoseny, Mohamed
collection PubMed
description At present times, healthcare systems are updated with advanced capabilities like machine learning (ML), data mining and artificial intelligence to offer human with more intelligent and expert healthcare services. This paper introduces an intelligent prediction and classification system for healthcare, namely Density based Feature Selection (DFS) with Ant Colony based Optimization (D-ACO) algorithm for chronic kidney disease (CKD). The proposed intelligent system eliminates irrelevant or redundant features by DFS in prior to the ACO based classifier construction. The proposed D-ACO framework three phases namely preprocessing, Feature Selection (FS) and classification. Furthermore, the D-ACO algorithm is tested using benchmark CKD dataset and the performance are investigated based on different evaluation factors. Comparing the D-ACO algorithm with existing methods, the presented intelligent system outperformed the other methodologies with a significant improvisation in classification accuracy using fewer features.
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spelling pubmed-66101222019-07-14 Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease Elhoseny, Mohamed Shankar, K. Uthayakumar, J. Sci Rep Article At present times, healthcare systems are updated with advanced capabilities like machine learning (ML), data mining and artificial intelligence to offer human with more intelligent and expert healthcare services. This paper introduces an intelligent prediction and classification system for healthcare, namely Density based Feature Selection (DFS) with Ant Colony based Optimization (D-ACO) algorithm for chronic kidney disease (CKD). The proposed intelligent system eliminates irrelevant or redundant features by DFS in prior to the ACO based classifier construction. The proposed D-ACO framework three phases namely preprocessing, Feature Selection (FS) and classification. Furthermore, the D-ACO algorithm is tested using benchmark CKD dataset and the performance are investigated based on different evaluation factors. Comparing the D-ACO algorithm with existing methods, the presented intelligent system outperformed the other methodologies with a significant improvisation in classification accuracy using fewer features. Nature Publishing Group UK 2019-07-03 /pmc/articles/PMC6610122/ /pubmed/31270387 http://dx.doi.org/10.1038/s41598-019-46074-2 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Elhoseny, Mohamed
Shankar, K.
Uthayakumar, J.
Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease
title Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease
title_full Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease
title_fullStr Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease
title_full_unstemmed Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease
title_short Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease
title_sort intelligent diagnostic prediction and classification system for chronic kidney disease
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6610122/
https://www.ncbi.nlm.nih.gov/pubmed/31270387
http://dx.doi.org/10.1038/s41598-019-46074-2
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