Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis
Abstract 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 a...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-27340-2 |
| _version_ | 1797952524766937088 |
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| author | Amin Mansoori Toktam Sahranavard Zeinab Sadat Hosseini Sara Saffar Soflaei Negar Emrani Eisa Nazar Melika Gharizadeh Zahra Khorasanchi Sohrab Effati Mark Ghamsary Gordon Ferns Habibollah Esmaily Majid Ghayour Mobarhan |
| author_facet | Amin Mansoori Toktam Sahranavard Zeinab Sadat Hosseini Sara Saffar Soflaei Negar Emrani Eisa Nazar Melika Gharizadeh Zahra Khorasanchi Sohrab Effati Mark Ghamsary Gordon Ferns Habibollah Esmaily Majid Ghayour Mobarhan |
| author_sort | Amin Mansoori |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-10T22:47:44Z |
| format | Article |
| id | doaj.art-0207c6dc6d6b4004ab463527e03edbd7 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-10T22:47:44Z |
| publishDate | 2023-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-0207c6dc6d6b4004ab463527e03edbd72023-01-15T12:11:16ZengNature PortfolioScientific Reports2045-23222023-01-0113111110.1038/s41598-022-27340-2Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysisAmin Mansoori0Toktam Sahranavard1Zeinab Sadat Hosseini2Sara Saffar Soflaei3Negar Emrani4Eisa Nazar5Melika Gharizadeh6Zahra Khorasanchi7Sohrab Effati8Mark Ghamsary9Gordon Ferns10Habibollah Esmaily11Majid Ghayour Mobarhan12International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical SciencesInternational UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical SciencesFaculty of Medicine, Islamic Azad University of MashhadInternational UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical SciencesStudent Research Committee, School of Medicine, Mashhad University of Medical ScienceInternational UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical SciencesStudent Research Committee, School of Paramedical Sciences, Mashhad University of Medical SciencesStudent Research Committee, School of Medicine, Mashhad University of Medical ScienceDepartment of Applied Mathematics, Ferdowsi University of MashhadSchool of Public Health, Loma Linda UniversityDivision of Medical Education, Brighton and Sussex Medical SchoolSocial Determinants of Health Research Center, Mashhad University of Medical SciencesInternational UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical SciencesAbstract 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.https://doi.org/10.1038/s41598-022-27340-2 |
| spellingShingle | Amin Mansoori Toktam Sahranavard Zeinab Sadat Hosseini Sara Saffar Soflaei Negar Emrani Eisa Nazar Melika Gharizadeh Zahra Khorasanchi Sohrab Effati Mark Ghamsary Gordon Ferns Habibollah Esmaily Majid Ghayour Mobarhan Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis Scientific Reports |
| 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 |
| url | https://doi.org/10.1038/s41598-022-27340-2 |
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