Cargando…
Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis
Type 2 Diabetes Mellitus (T2DM) is a significant public health problem globally. The diagnosis and management of diabetes are critical to reduce the diabetes complications including cardiovascular disease and cancer. This study was designed to assess the potential association between T2DM and routin...
Autores principales: | , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9837189/ https://www.ncbi.nlm.nih.gov/pubmed/36635303 http://dx.doi.org/10.1038/s41598-022-27340-2 |
_version_ | 1784869022181883904 |
---|---|
author | Mansoori, Amin Sahranavard, Toktam Hosseini, Zeinab Sadat Soflaei, Sara Saffar Emrani, Negar Nazar, Eisa Gharizadeh, Melika Khorasanchi, Zahra Effati, Sohrab Ghamsary, Mark Ferns, Gordon Esmaily, Habibollah Mobarhan, Majid Ghayour |
author_facet | Mansoori, Amin Sahranavard, Toktam Hosseini, Zeinab Sadat Soflaei, Sara Saffar Emrani, Negar Nazar, Eisa Gharizadeh, Melika Khorasanchi, Zahra Effati, Sohrab Ghamsary, Mark Ferns, Gordon Esmaily, Habibollah Mobarhan, Majid Ghayour |
author_sort | Mansoori, Amin |
collection | PubMed |
description | Type 2 Diabetes Mellitus (T2DM) is a significant public health problem globally. The diagnosis and management of diabetes are critical to reduce the diabetes complications including cardiovascular disease and cancer. This study was designed to assess the potential association between T2DM and routinely measured hematological parameters. This study was a subsample of 9000 adults aged 35–65 years recruited as part of Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study. Machine learning techniques including logistic regression (LR), decision tree (DT) and bootstrap forest (BF) algorithms were applied to analyze data. All data analyses were performed using SPSS version 22 and SAS JMP Pro version 13 at a significant level of 0.05. Based on the performance indices, the BF model gave high accuracy, precision, specificity, and AUC. Previous studies suggested the positive relationship of triglyceride-glucose (TyG) index with T2DM, so we considered the association of TyG index with hematological factors. We found this association was aligned with their results regarding T2DM, except MCHC. The most effective factors in the BF model were age and WBC (white blood cell). The BF model represented a better performance to predict T2DM. Our model provides valuable information to predict T2DM like age and WBC. |
format | Online Article Text |
id | pubmed-9837189 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98371892023-01-14 Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis Mansoori, Amin Sahranavard, Toktam Hosseini, Zeinab Sadat Soflaei, Sara Saffar Emrani, Negar Nazar, Eisa Gharizadeh, Melika Khorasanchi, Zahra Effati, Sohrab Ghamsary, Mark Ferns, Gordon Esmaily, Habibollah Mobarhan, Majid Ghayour Sci Rep Article Type 2 Diabetes Mellitus (T2DM) is a significant public health problem globally. The diagnosis and management of diabetes are critical to reduce the diabetes complications including cardiovascular disease and cancer. This study was designed to assess the potential association between T2DM and routinely measured hematological parameters. This study was a subsample of 9000 adults aged 35–65 years recruited as part of Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study. Machine learning techniques including logistic regression (LR), decision tree (DT) and bootstrap forest (BF) algorithms were applied to analyze data. All data analyses were performed using SPSS version 22 and SAS JMP Pro version 13 at a significant level of 0.05. Based on the performance indices, the BF model gave high accuracy, precision, specificity, and AUC. Previous studies suggested the positive relationship of triglyceride-glucose (TyG) index with T2DM, so we considered the association of TyG index with hematological factors. We found this association was aligned with their results regarding T2DM, except MCHC. The most effective factors in the BF model were age and WBC (white blood cell). The BF model represented a better performance to predict T2DM. Our model provides valuable information to predict T2DM like age and WBC. Nature Publishing Group UK 2023-01-12 /pmc/articles/PMC9837189/ /pubmed/36635303 http://dx.doi.org/10.1038/s41598-022-27340-2 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/) . |
spellingShingle | Article Mansoori, Amin Sahranavard, Toktam Hosseini, Zeinab Sadat Soflaei, Sara Saffar Emrani, Negar Nazar, Eisa Gharizadeh, Melika Khorasanchi, Zahra Effati, Sohrab Ghamsary, Mark Ferns, Gordon Esmaily, Habibollah Mobarhan, Majid Ghayour Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis |
title | Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis |
title_full | Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis |
title_fullStr | Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis |
title_full_unstemmed | Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis |
title_short | Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis |
title_sort | prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9837189/ https://www.ncbi.nlm.nih.gov/pubmed/36635303 http://dx.doi.org/10.1038/s41598-022-27340-2 |
work_keys_str_mv | AT mansooriamin predictionoftype2diabetesmellitususinghematologicalfactorsbasedonmachinelearningapproachesacohortstudyanalysis AT sahranavardtoktam predictionoftype2diabetesmellitususinghematologicalfactorsbasedonmachinelearningapproachesacohortstudyanalysis AT hosseinizeinabsadat predictionoftype2diabetesmellitususinghematologicalfactorsbasedonmachinelearningapproachesacohortstudyanalysis AT soflaeisarasaffar predictionoftype2diabetesmellitususinghematologicalfactorsbasedonmachinelearningapproachesacohortstudyanalysis AT emraninegar predictionoftype2diabetesmellitususinghematologicalfactorsbasedonmachinelearningapproachesacohortstudyanalysis AT nazareisa predictionoftype2diabetesmellitususinghematologicalfactorsbasedonmachinelearningapproachesacohortstudyanalysis AT gharizadehmelika predictionoftype2diabetesmellitususinghematologicalfactorsbasedonmachinelearningapproachesacohortstudyanalysis AT khorasanchizahra predictionoftype2diabetesmellitususinghematologicalfactorsbasedonmachinelearningapproachesacohortstudyanalysis AT effatisohrab predictionoftype2diabetesmellitususinghematologicalfactorsbasedonmachinelearningapproachesacohortstudyanalysis AT ghamsarymark predictionoftype2diabetesmellitususinghematologicalfactorsbasedonmachinelearningapproachesacohortstudyanalysis AT fernsgordon predictionoftype2diabetesmellitususinghematologicalfactorsbasedonmachinelearningapproachesacohortstudyanalysis AT esmailyhabibollah predictionoftype2diabetesmellitususinghematologicalfactorsbasedonmachinelearningapproachesacohortstudyanalysis AT mobarhanmajidghayour predictionoftype2diabetesmellitususinghematologicalfactorsbasedonmachinelearningapproachesacohortstudyanalysis |