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An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications

Background: Continuous surveillance helps people with diabetes live better lives. A wide range of technologies, including the Internet of Things (IoT), modern communications, and artificial intelligence (AI), can assist in lowering the expense of health services. Due to numerous communication system...

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Autores principales: Menon, Sindhu P., Shukla, Prashant Kumar, Sethi, Priyanka, Alasiry, Areej, Marzougui, Mehrez, Alouane, M. Turki-Hadj, Khan, Arfat Ahmad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10052330/
https://www.ncbi.nlm.nih.gov/pubmed/36991714
http://dx.doi.org/10.3390/s23063004
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author Menon, Sindhu P.
Shukla, Prashant Kumar
Sethi, Priyanka
Alasiry, Areej
Marzougui, Mehrez
Alouane, M. Turki-Hadj
Khan, Arfat Ahmad
author_facet Menon, Sindhu P.
Shukla, Prashant Kumar
Sethi, Priyanka
Alasiry, Areej
Marzougui, Mehrez
Alouane, M. Turki-Hadj
Khan, Arfat Ahmad
author_sort Menon, Sindhu P.
collection PubMed
description Background: Continuous surveillance helps people with diabetes live better lives. A wide range of technologies, including the Internet of Things (IoT), modern communications, and artificial intelligence (AI), can assist in lowering the expense of health services. Due to numerous communication systems, it is now possible to provide customized and distant healthcare. Main problem: Healthcare data grows daily, making storage and processing challenging. We provide intelligent healthcare structures for smart e-health apps to solve the aforesaid problem. The 5G network must offer advanced healthcare services to meet important requirements like large bandwidth and excellent energy efficacy. Methodology: This research suggested an intelligent system for diabetic patient tracking based on machine learning (ML). The architectural components comprised smartphones, sensors, and smart devices, to gather body dimensions. Then, the preprocessed data is normalized using the normalization procedure. To extract features, we use linear discriminant analysis (LDA). To establish a diagnosis, the intelligent system conducted data classification utilizing the suggested advanced-spatial-vector-based Random Forest (ASV-RF) in conjunction with particle swarm optimization (PSO). Results: Compared to other techniques, the simulation’s outcomes demonstrate that the suggested approach offers greater accuracy.
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spelling pubmed-100523302023-03-30 An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications Menon, Sindhu P. Shukla, Prashant Kumar Sethi, Priyanka Alasiry, Areej Marzougui, Mehrez Alouane, M. Turki-Hadj Khan, Arfat Ahmad Sensors (Basel) Article Background: Continuous surveillance helps people with diabetes live better lives. A wide range of technologies, including the Internet of Things (IoT), modern communications, and artificial intelligence (AI), can assist in lowering the expense of health services. Due to numerous communication systems, it is now possible to provide customized and distant healthcare. Main problem: Healthcare data grows daily, making storage and processing challenging. We provide intelligent healthcare structures for smart e-health apps to solve the aforesaid problem. The 5G network must offer advanced healthcare services to meet important requirements like large bandwidth and excellent energy efficacy. Methodology: This research suggested an intelligent system for diabetic patient tracking based on machine learning (ML). The architectural components comprised smartphones, sensors, and smart devices, to gather body dimensions. Then, the preprocessed data is normalized using the normalization procedure. To extract features, we use linear discriminant analysis (LDA). To establish a diagnosis, the intelligent system conducted data classification utilizing the suggested advanced-spatial-vector-based Random Forest (ASV-RF) in conjunction with particle swarm optimization (PSO). Results: Compared to other techniques, the simulation’s outcomes demonstrate that the suggested approach offers greater accuracy. MDPI 2023-03-10 /pmc/articles/PMC10052330/ /pubmed/36991714 http://dx.doi.org/10.3390/s23063004 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Menon, Sindhu P.
Shukla, Prashant Kumar
Sethi, Priyanka
Alasiry, Areej
Marzougui, Mehrez
Alouane, M. Turki-Hadj
Khan, Arfat Ahmad
An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications
title An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications
title_full An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications
title_fullStr An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications
title_full_unstemmed An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications
title_short An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications
title_sort intelligent diabetic patient tracking system based on machine learning for e-health applications
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10052330/
https://www.ncbi.nlm.nih.gov/pubmed/36991714
http://dx.doi.org/10.3390/s23063004
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