Performance Analysis and Assessment of Type 2 Diabetes Screening Scores in Patients with Non-Alcoholic Fatty Liver Disease

Norma Latif Fitriyani, Muhammad Syafrudin (), Siti Maghfirotul Ulyah, Ganjar Alfian, Syifa Latif Qolbiyani, Chuan-Kai Yang, Jongtae Rhee and Muhammad Anshari
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Norma Latif Fitriyani: Department of Data Science, Sejong University, Seoul 05006, Republic of Korea
Muhammad Syafrudin: Department of Artificial Intelligence, Sejong University, Seoul 05006, Republic of 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
Chuan-Kai Yang: Department of Information Management, National Taiwan University of Science and Technology, Taipei City 106335, Taiwan
Jongtae Rhee: Department of Industrial and Systems Engineering, Dongguk University, Seoul 04620, Republic of Korea
Muhammad Anshari: School of Business & Economics, Universiti Brunei Darussalam, Bandar Seri Begawan BE1410, Brunei

Mathematics, 2023, vol. 11, issue 10, 1-25

Abstract: Type 2 diabetes (T2D) and non-alcoholic fatty liver disease (NAFLD) are worldwide chronic diseases that have strong relationships with one another and commonly exist together. Type 2 diabetes is considered one of the risk factors for NAFLD, so its occurrence in people with NAFLD is highly likely. As the high and increasing number of T2D and NAFLD, which potentially followed by existing together number, an analysis and assessment of T2D screening scores in people with NAFLD is necessary to be done. To prevent this potential case, an effective early prediction model is also required to be developed, which could help the patients avoid the dangers of both existing diseases. Therefore, in this study, analysis and assessment of T2D screening scores in people with NAFLD and the early prediction model utilizing a forward logistic regression-based feature selection method and multi-layer perceptrons are proposed. Our analysis and assessment results showed that the prevalence of T2D among patients with NAFLD was 8.13% (for prediabetes) and 37.19% (for diabetes) in two population-based NAFLD datasets. The variables related to clinical tests, such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), and systolic blood pressure (SBP), were found to be statistically significant predictors ( p -values < 0.001) that indicate a strong association with T2D among patients with NAFLD in both the prediabetes and diabetes NAFLD datasets. Finally, our proposed model showed the best performance in terms of all performance evaluation metrics compared to existing various machine learning models and also the models using variables recommended by WHO/CDC/ADA, with achieved accuracy as much as 92.11% and 83.05% and its improvement scores after feature selection of 1.35% and 5.35%, for the first and second dataset, respectively.

Keywords: Type 2 diabetes (T2D); non-alcoholic fatty liver disease (NAFLD); T2D analysis and assessment; T2D screening scores; early T2D prediction model; feature selection; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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