A Comprehensive Analysis of Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian Screening Scores for Diabetes Risk Assessment and Prediction

Norma Latif Fitriyani, Muhammad Syafrudin (), Siti Maghfirotul Ulyah, Ganjar Alfian, Syifa Latif Qolbiyani and Muhammad Anshari
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Norma Latif Fitriyani: Department of Data Science, Sejong University, Seoul 05006, Korea
Muhammad Syafrudin: Department of Artificial Intelligence, Sejong University, Seoul 05006, Korea
Siti Maghfirotul Ulyah: Department of Mathematics, Khalifa University, Abu Dhabi 127788, United Arab Emirates
Ganjar Alfian: Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
Syifa Latif Qolbiyani: Department of Community Development, Universitas Sebelas Maret, Surakarta 57126, Indonesia
Muhammad Anshari: School of Business & Economics, Universiti Brunei Darussalam, Bandar Seri Begawan BE1410, Brunei

Mathematics, 2022, vol. 10, issue 21, 1-23

Abstract: 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 ( p -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.

Keywords: risk assessment; prediction model; diabetes; statistical method; screening scores; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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