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Optimizing diabetes classification with a machine learning-based framework
BACKGROUND: Diabetes is a metabolic disorder usually caused by insufficient secretion of insulin from the pancreas or insensitivity of cells to insulin, resulting in long-term elevated blood sugar levels in patients. Patients usually present with frequent urination, thirst, and hunger. If left untre...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10644638/ https://www.ncbi.nlm.nih.gov/pubmed/37957549 http://dx.doi.org/10.1186/s12859-023-05467-x |
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author | Feng, Xin Cai, Yihuai Xin, Ruihao |
author_facet | Feng, Xin Cai, Yihuai Xin, Ruihao |
author_sort | Feng, Xin |
collection | PubMed |
description | BACKGROUND: Diabetes is a metabolic disorder usually caused by insufficient secretion of insulin from the pancreas or insensitivity of cells to insulin, resulting in long-term elevated blood sugar levels in patients. Patients usually present with frequent urination, thirst, and hunger. If left untreated, it can lead to various complications that can affect essential organs and even endanger life. Therefore, developing an intelligent diagnosis framework for diabetes is necessary. RESULT: This paper proposes a machine learning-based diabetes classification framework machine learning optimized GAN. The framework encompasses several methodological approaches to address the diverse challenges encountered during the analysis. These approaches encompass the implementation of the mean and median joint filling method for handling missing values, the application of the cap method for outlier processing, and the utilization of SMOTEENN to mitigate sample imbalance. Additionally, the framework incorporates the employment of the proposed Diabetes Classification Model based on Generative Adversarial Network and employs logistic regression for detailed feature analysis. The effectiveness of the framework is evaluated using both the PIMA dataset and the diabetes dataset obtained from the GEO database. The experimental findings showcase our model achieved exceptional results, including a binary classification accuracy of 96.27%, tertiary classification accuracy of 99.31%, precision and f1 score of 0.9698, recall of 0.9698, and an AUC of 0.9702. CONCLUSION: The experimental results show that the framework proposed in this paper can accurately classify diabetes and provide new ideas for intelligent diagnosis of diabetes. |
format | Online Article Text |
id | pubmed-10644638 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106446382023-11-13 Optimizing diabetes classification with a machine learning-based framework Feng, Xin Cai, Yihuai Xin, Ruihao BMC Bioinformatics Research BACKGROUND: Diabetes is a metabolic disorder usually caused by insufficient secretion of insulin from the pancreas or insensitivity of cells to insulin, resulting in long-term elevated blood sugar levels in patients. Patients usually present with frequent urination, thirst, and hunger. If left untreated, it can lead to various complications that can affect essential organs and even endanger life. Therefore, developing an intelligent diagnosis framework for diabetes is necessary. RESULT: This paper proposes a machine learning-based diabetes classification framework machine learning optimized GAN. The framework encompasses several methodological approaches to address the diverse challenges encountered during the analysis. These approaches encompass the implementation of the mean and median joint filling method for handling missing values, the application of the cap method for outlier processing, and the utilization of SMOTEENN to mitigate sample imbalance. Additionally, the framework incorporates the employment of the proposed Diabetes Classification Model based on Generative Adversarial Network and employs logistic regression for detailed feature analysis. The effectiveness of the framework is evaluated using both the PIMA dataset and the diabetes dataset obtained from the GEO database. The experimental findings showcase our model achieved exceptional results, including a binary classification accuracy of 96.27%, tertiary classification accuracy of 99.31%, precision and f1 score of 0.9698, recall of 0.9698, and an AUC of 0.9702. CONCLUSION: The experimental results show that the framework proposed in this paper can accurately classify diabetes and provide new ideas for intelligent diagnosis of diabetes. BioMed Central 2023-11-13 /pmc/articles/PMC10644638/ /pubmed/37957549 http://dx.doi.org/10.1186/s12859-023-05467-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Feng, Xin Cai, Yihuai Xin, Ruihao Optimizing diabetes classification with a machine learning-based framework |
title | Optimizing diabetes classification with a machine learning-based framework |
title_full | Optimizing diabetes classification with a machine learning-based framework |
title_fullStr | Optimizing diabetes classification with a machine learning-based framework |
title_full_unstemmed | Optimizing diabetes classification with a machine learning-based framework |
title_short | Optimizing diabetes classification with a machine learning-based framework |
title_sort | optimizing diabetes classification with a machine learning-based framework |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10644638/ https://www.ncbi.nlm.nih.gov/pubmed/37957549 http://dx.doi.org/10.1186/s12859-023-05467-x |
work_keys_str_mv | AT fengxin optimizingdiabetesclassificationwithamachinelearningbasedframework AT caiyihuai optimizingdiabetesclassificationwithamachinelearningbasedframework AT xinruihao optimizingdiabetesclassificationwithamachinelearningbasedframework |