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

Author

Listed:
  • 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
    Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Surabaya 60115, Indonesia)

  • 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)

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.

Suggested Citation

  • Norma Latif Fitriyani & Muhammad Syafrudin & Siti Maghfirotul Ulyah & Ganjar Alfian & Syifa Latif Qolbiyani & Chuan-Kai Yang & Jongtae Rhee & Muhammad Anshari, 2023. "Performance Analysis and Assessment of Type 2 Diabetes Screening Scores in Patients with Non-Alcoholic Fatty Liver Disease," Mathematics, MDPI, vol. 11(10), pages 1-25, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2266-:d:1145365
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    References listed on IDEAS

    as
    1. Aishwariya Dutta & Md. Kamrul Hasan & Mohiuddin Ahmad & Md. Abdul Awal & Md. Akhtarul Islam & Mehedi Masud & Hossam Meshref, 2022. "Early Prediction of Diabetes Using an Ensemble of Machine Learning Models," IJERPH, MDPI, vol. 19(19), pages 1-25, September.
    2. Henock M. Deberneh & Intaek Kim, 2021. "Prediction of Type 2 Diabetes Based on Machine Learning Algorithm," IJERPH, MDPI, vol. 18(6), pages 1-14, March.
    3. Norma Latif Fitriyani & Muhammad Syafrudin & Siti Maghfirotul Ulyah & Ganjar Alfian & Syifa Latif Qolbiyani & Muhammad Anshari, 2022. "A Comprehensive Analysis of Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian Screening Scores for Diabetes Risk Assessment and Prediction," Mathematics, MDPI, vol. 10(21), pages 1-23, October.
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