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An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study
Diabetes is a long-lasting disease triggered by expanded sugar levels in human blood and can affect various organs if left untreated. It contributes to heart disease, kidney issues, damaged nerves, damaged blood vessels, and blindness. Timely disease prediction can save precious lives and enable hea...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9324493/ https://www.ncbi.nlm.nih.gov/pubmed/35890927 http://dx.doi.org/10.3390/s22145247 |
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author | Laila, Umm e Mahboob, Khalid Khan, Abdul Wahid Khan, Faheem Taekeun, Whangbo |
author_facet | Laila, Umm e Mahboob, Khalid Khan, Abdul Wahid Khan, Faheem Taekeun, Whangbo |
author_sort | Laila, Umm e |
collection | PubMed |
description | Diabetes is a long-lasting disease triggered by expanded sugar levels in human blood and can affect various organs if left untreated. It contributes to heart disease, kidney issues, damaged nerves, damaged blood vessels, and blindness. Timely disease prediction can save precious lives and enable healthcare advisors to take care of the conditions. Most diabetic patients know little about the risk factors they face before diagnosis. Nowadays, hospitals deploy basic information systems, which generate vast amounts of data that cannot be converted into proper/useful information and cannot be used to support decision making for clinical purposes. There are different automated techniques available for the earlier prediction of disease. Ensemble learning is a data analysis technique that combines multiple techniques into a single optimal predictive system to evaluate bias and variation, and to improve predictions. Diabetes data, which included 17 variables, were gathered from the UCI repository of various datasets. The predictive models used in this study include AdaBoost, Bagging, and Random Forest, to compare the precision, recall, classification accuracy, and F1-score. Finally, the Random Forest Ensemble Method had the best accuracy (97%), whereas the AdaBoost and Bagging algorithms had lower accuracy, precision, recall, and F1-scores. |
format | Online Article Text |
id | pubmed-9324493 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93244932022-07-27 An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study Laila, Umm e Mahboob, Khalid Khan, Abdul Wahid Khan, Faheem Taekeun, Whangbo Sensors (Basel) Article Diabetes is a long-lasting disease triggered by expanded sugar levels in human blood and can affect various organs if left untreated. It contributes to heart disease, kidney issues, damaged nerves, damaged blood vessels, and blindness. Timely disease prediction can save precious lives and enable healthcare advisors to take care of the conditions. Most diabetic patients know little about the risk factors they face before diagnosis. Nowadays, hospitals deploy basic information systems, which generate vast amounts of data that cannot be converted into proper/useful information and cannot be used to support decision making for clinical purposes. There are different automated techniques available for the earlier prediction of disease. Ensemble learning is a data analysis technique that combines multiple techniques into a single optimal predictive system to evaluate bias and variation, and to improve predictions. Diabetes data, which included 17 variables, were gathered from the UCI repository of various datasets. The predictive models used in this study include AdaBoost, Bagging, and Random Forest, to compare the precision, recall, classification accuracy, and F1-score. Finally, the Random Forest Ensemble Method had the best accuracy (97%), whereas the AdaBoost and Bagging algorithms had lower accuracy, precision, recall, and F1-scores. MDPI 2022-07-13 /pmc/articles/PMC9324493/ /pubmed/35890927 http://dx.doi.org/10.3390/s22145247 Text en © 2022 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 Laila, Umm e Mahboob, Khalid Khan, Abdul Wahid Khan, Faheem Taekeun, Whangbo An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study |
title | An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study |
title_full | An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study |
title_fullStr | An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study |
title_full_unstemmed | An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study |
title_short | An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study |
title_sort | ensemble approach to predict early-stage diabetes risk using machine learning: an empirical study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9324493/ https://www.ncbi.nlm.nih.gov/pubmed/35890927 http://dx.doi.org/10.3390/s22145247 |
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