A Comprehensive Analysis of Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian Screening Scores for Diabetes Risk Assessment and Prediction
Risk assessment and developing predictive models for diabetes prevention is considered an important task. Therefore, we proposed to analyze and provide a comprehensive analysis of the performance of diabetes screening scores for risk assessment and prediction in five populations: the Chinese, Japane...
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| Format: | Article |
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MDPI AG
2022-10-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/21/4027 |
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| author | Norma Latif Fitriyani Muhammad Syafrudin Siti Maghfirotul Ulyah Ganjar Alfian Syifa Latif Qolbiyani Muhammad Anshari |
| author_facet | Norma Latif Fitriyani Muhammad Syafrudin Siti Maghfirotul Ulyah Ganjar Alfian Syifa Latif Qolbiyani Muhammad Anshari |
| author_sort | Norma Latif Fitriyani |
| collection | DOAJ |
| description | Risk assessment and developing predictive models for diabetes prevention is considered an important task. Therefore, we proposed to analyze and provide a comprehensive analysis of the performance of diabetes screening scores for risk assessment and prediction in five populations: the Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian populations, utilizing statistical and machine learning (ML) methods. Additionally, due to the present COVID-19 epidemic, it is necessary to investigate how diabetes and COVID-19 are related to one another. Thus, by using a sample of the Korean population, the interrelationship between diabetes and COVID-19 was further investigated. The results revealed that by using a statistical method, the optimal cut points among Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian populations were 6.205 mmol/L (FPG), 5.523 mmol/L (FPG), and 5.375% (HbA1c), 150.50–106.50 mg/dL (FBS), 123.50 mg/dL (2hPG), and 107.50 mg/dL (FBG), respectively, with AUC scores of 0.97, 0.80, 0.78, 0.85, 0.79, and 0.905. The results also confirmed that diabetes has a significant relationship with COVID-19 in the Korean population (<i>p</i>-value 0.001), with an adjusted OR of 1.21. Finally, the overall best ML models were performed by Naïve Bayes with AUC scores of 0.736, 0.75, and 0.83 in the Japanese, Korean, and Trinidadian populations, respectively. |
| first_indexed | 2024-03-09T18:52:11Z |
| format | Article |
| id | doaj.art-b5e01399785143d08d00a9eea62daddf |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T18:52:11Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-b5e01399785143d08d00a9eea62daddf2023-11-24T05:43:45ZengMDPI AGMathematics2227-73902022-10-011021402710.3390/math10214027A Comprehensive Analysis of Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian Screening Scores for Diabetes Risk Assessment and PredictionNorma Latif Fitriyani0Muhammad Syafrudin1Siti Maghfirotul Ulyah2Ganjar Alfian3Syifa Latif Qolbiyani4Muhammad Anshari5Department of Data Science, Sejong University, Seoul 05006, KoreaDepartment of Artificial Intelligence, Sejong University, Seoul 05006, KoreaDepartment of Mathematics, Khalifa University, Abu Dhabi 127788, United Arab EmiratesDepartment of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaDepartment of Community Development, Universitas Sebelas Maret, Surakarta 57126, IndonesiaSchool of Business & Economics, Universiti Brunei Darussalam, Bandar Seri Begawan BE1410, BruneiRisk assessment and developing predictive models for diabetes prevention is considered an important task. Therefore, we proposed to analyze and provide a comprehensive analysis of the performance of diabetes screening scores for risk assessment and prediction in five populations: the Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian populations, utilizing statistical and machine learning (ML) methods. Additionally, due to the present COVID-19 epidemic, it is necessary to investigate how diabetes and COVID-19 are related to one another. Thus, by using a sample of the Korean population, the interrelationship between diabetes and COVID-19 was further investigated. The results revealed that by using a statistical method, the optimal cut points among Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian populations were 6.205 mmol/L (FPG), 5.523 mmol/L (FPG), and 5.375% (HbA1c), 150.50–106.50 mg/dL (FBS), 123.50 mg/dL (2hPG), and 107.50 mg/dL (FBG), respectively, with AUC scores of 0.97, 0.80, 0.78, 0.85, 0.79, and 0.905. The results also confirmed that diabetes has a significant relationship with COVID-19 in the Korean population (<i>p</i>-value 0.001), with an adjusted OR of 1.21. Finally, the overall best ML models were performed by Naïve Bayes with AUC scores of 0.736, 0.75, and 0.83 in the Japanese, Korean, and Trinidadian populations, respectively.https://www.mdpi.com/2227-7390/10/21/4027risk assessmentprediction modeldiabetesstatistical methodscreening scoresmachine learning |
| spellingShingle | Norma Latif Fitriyani Muhammad Syafrudin Siti Maghfirotul Ulyah Ganjar Alfian Syifa Latif Qolbiyani Muhammad Anshari A Comprehensive Analysis of Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian Screening Scores for Diabetes Risk Assessment and Prediction Mathematics risk assessment prediction model diabetes statistical method screening scores machine learning |
| title | A Comprehensive Analysis of Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian Screening Scores for Diabetes Risk Assessment and Prediction |
| title_full | A Comprehensive Analysis of Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian Screening Scores for Diabetes Risk Assessment and Prediction |
| title_fullStr | A Comprehensive Analysis of Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian Screening Scores for Diabetes Risk Assessment and Prediction |
| title_full_unstemmed | A Comprehensive Analysis of Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian Screening Scores for Diabetes Risk Assessment and Prediction |
| title_short | A Comprehensive Analysis of Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian Screening Scores for Diabetes Risk Assessment and Prediction |
| title_sort | comprehensive analysis of chinese japanese korean us pima indian and trinidadian screening scores for diabetes risk assessment and prediction |
| topic | risk assessment prediction model diabetes statistical method screening scores machine learning |
| url | https://www.mdpi.com/2227-7390/10/21/4027 |
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