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Performance analysis and comparison of Machine Learning and LoRa-based Healthcare model
Diabetes Mellitus (DM) is a widespread condition that is one of the main causes of health disasters around the world, and health monitoring is one of the sustainable development topics. Currently, the Internet of Things (IoT) and Machine Learning (ML) technologies work together to provide a reliable...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer London
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9989556/ https://www.ncbi.nlm.nih.gov/pubmed/37192938 http://dx.doi.org/10.1007/s00521-023-08411-5 |
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author | Verma, Navneet Singh, Sukhdip Prasad, Devendra |
author_facet | Verma, Navneet Singh, Sukhdip Prasad, Devendra |
author_sort | Verma, Navneet |
collection | PubMed |
description | Diabetes Mellitus (DM) is a widespread condition that is one of the main causes of health disasters around the world, and health monitoring is one of the sustainable development topics. Currently, the Internet of Things (IoT) and Machine Learning (ML) technologies work together to provide a reliable method of monitoring and predicting Diabetes Mellitus. In this paper, we present the performance of a model for patient real-time data collection that employs the Hybrid Enhanced Adaptive Data Rate (HEADR) algorithm for the Long-Range (LoRa) protocol of the IoT. On the Contiki Cooja simulator, the LoRa protocol's performance is measured in terms of high dissemination and dynamic data transmission range allocation. Furthermore, by employing classification methods for the detection of diabetes severity levels on acquired data via the LoRa (HEADR) protocol, Machine Learning prediction takes place. For prediction, a variety of Machine Learning classifiers are employed, and the final results are compared with the already existing models where the Random Forest and Decision Tree classifiers outperform the others in terms of precision, recall, F-measure, and receiver operating curve (ROC) in the Python programming language. We also discovered that using k-fold cross-validation on k-neighbors, Logistic regression (LR), and Gaussian Nave Bayes (GNB) classifiers boosted the accuracy. |
format | Online Article Text |
id | pubmed-9989556 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer London |
record_format | MEDLINE/PubMed |
spelling | pubmed-99895562023-03-07 Performance analysis and comparison of Machine Learning and LoRa-based Healthcare model Verma, Navneet Singh, Sukhdip Prasad, Devendra Neural Comput Appl Original Article Diabetes Mellitus (DM) is a widespread condition that is one of the main causes of health disasters around the world, and health monitoring is one of the sustainable development topics. Currently, the Internet of Things (IoT) and Machine Learning (ML) technologies work together to provide a reliable method of monitoring and predicting Diabetes Mellitus. In this paper, we present the performance of a model for patient real-time data collection that employs the Hybrid Enhanced Adaptive Data Rate (HEADR) algorithm for the Long-Range (LoRa) protocol of the IoT. On the Contiki Cooja simulator, the LoRa protocol's performance is measured in terms of high dissemination and dynamic data transmission range allocation. Furthermore, by employing classification methods for the detection of diabetes severity levels on acquired data via the LoRa (HEADR) protocol, Machine Learning prediction takes place. For prediction, a variety of Machine Learning classifiers are employed, and the final results are compared with the already existing models where the Random Forest and Decision Tree classifiers outperform the others in terms of precision, recall, F-measure, and receiver operating curve (ROC) in the Python programming language. We also discovered that using k-fold cross-validation on k-neighbors, Logistic regression (LR), and Gaussian Nave Bayes (GNB) classifiers boosted the accuracy. Springer London 2023-03-07 2023 /pmc/articles/PMC9989556/ /pubmed/37192938 http://dx.doi.org/10.1007/s00521-023-08411-5 Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Article Verma, Navneet Singh, Sukhdip Prasad, Devendra Performance analysis and comparison of Machine Learning and LoRa-based Healthcare model |
title | Performance analysis and comparison of Machine Learning and LoRa-based Healthcare model |
title_full | Performance analysis and comparison of Machine Learning and LoRa-based Healthcare model |
title_fullStr | Performance analysis and comparison of Machine Learning and LoRa-based Healthcare model |
title_full_unstemmed | Performance analysis and comparison of Machine Learning and LoRa-based Healthcare model |
title_short | Performance analysis and comparison of Machine Learning and LoRa-based Healthcare model |
title_sort | performance analysis and comparison of machine learning and lora-based healthcare model |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9989556/ https://www.ncbi.nlm.nih.gov/pubmed/37192938 http://dx.doi.org/10.1007/s00521-023-08411-5 |
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