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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10052330 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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