Penerapan Logika Fuzzy dalam Penanganan Penyakit Diabetes: Sistematik Literatur Review
Keywords:
Fuzzy logic, medical data, classification, diabetes, predictionAbstract
This study presents an overview of the use of fuzzy methods in the diagnosis and prediction of diabetes mellitus. Fuzzy logic approaches have attracted attention in diabetes management due to their ability to overcome uncertainty and vagueness in medical data. Through the Literature Review research method, related articles selected from online databases have been systematically analyzed to evaluate the various approaches that have been developed. Findings from the literature review show that the use of fuzzy methods in diabetes diagnosis and prediction has resulted in significant progress, with some studies achieving high levels of accuracy. However, some challenges such as dataset limitations and dependency on certain features were also identified. In addition, the potential and challenges of fuzzy logic in diabetes diagnosis are discussed, including opportunities to improve diagnostic interpretation and comorbidity of medical data.
References
Aamir, Khalid Mahmood, Laiba Sarfraz, Muhammad Ramzan, Muhammad Bilal, Jana Shafi, and Muhammad Attique. 2021. “A Fuzzy Rule-Based System for Classification of Diabetes.” Sensors 21(23): 8095. doi:10.3390/s21238095.
Algehyne, Ebrahem A., Muhammad Lawan Jibril, Naseh A. Algehainy, Osama Abdulaziz Alamri, and Abdullah K. Alzahrani. 2022.
“Fuzzy Neural Network Expert System with an Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm for Early Diagnosis of Breast Cancer in Saudi Arabia.” Big Data and Cognitive Computing 6(1): 13. doi:10.3390/bdcc6010013.
Aloke, Chinyere, Chinedu Ogbonnia Egwu, Patrick Maduabuchi Aja, Nwogo Ajuka Obasi, Jennifer Chukwu, Blessing Oluebube
Akumadu, Patience Nkemjika Ogbu, and Ikechukwu Achilonu. 2022. “Current Advances in the Management of Diabetes Mellitus.” Biomedicines 10(10): 2436. doi:10.3390/biomedicines10102436.
Antonio-Villa, Neftali Eduardo, Omar Yaxmehen Bello-Chavolla, Carlos A. Fermín-Martínez, Daniel Ramírez-García, Arsenio Vargas-Vázquez, Martín Roberto Basile-Alvarez, Alejandra Núñez-Luna, et al. 2024. “Diabetes Subgroups and Sociodemographic Inequalities in Mexico: A Cross-Sectional Analysis of Nationally Representative Surveys from 2016 to 2022.” The Lancet Regional Health - Americas 33: 100732. doi:10.1016/j.lana.2024.100732.
Aynur Jabiyeva, Aynur Jabiyeva, and Mahabbat Khudaverdiyeva Mahabbat Khudaverdiyeva. 2023. “APPLICATION OF FUZZY LOGIC IN COMPUTER SYSTEMS OF MEDICAL DIAGNOSIS.” Socio World-Social Research & Behavioral Sciences 12(02): 17–25. doi:10.36962/SWD12022023-17.
Bansal, Vipul, Ankit Bhardwaj, Jitendra Singh, Devvret Verma, Mohit Tiwari, and Someshwar Siddi. 2023. “Using Artificial Intelligence to Integrate Machine Learning, Fuzzy Logic, and The IOT as A Cybersecurity System.” In 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), IEEE, 762–69. doi:10.1109/ICACITE57410.2023.10182967.
Baradaran, Vahid, and Elaheh Ghorbani. 2020. “Development of Fuzzy Exploratory Factor Analysis for Designing an E-Learning Service Quality Assessment Model.” International Journal of Fuzzy Systems 22(6): 1772–85. doi:10.1007/s40815-020-00901-1.
Casalino, Gabriella, Giovanna Castellano, Uzay Kaymak, and Gianluca Zaza. 2021. “Balancing Accuracy and Interpretability through Neuro-Fuzzy Models for Cardiovascular Risk Assessment.” In 2021 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 1–8. doi:10.1109/SSCI50451.2021.9660104.
Chaki, Jyotismita. 2023. “A Fuzzy Logic-Based Approach to Handle Uncertainty in Artificial Intelligence.” In , 47–69. doi:10.1007/978-981-99-5333-2_5.
Cleophas, Ton J., and Aeilko H. Zwinderman. 2020. “Fuzzy Logic for Improved Precision of Dose-Response Data (8 Induction Dosages).” In Machine Learning in Medicine – A Complete Overview, Cham: Springer International Publishing, 503–9. doi:10.1007/978-3-030-33970-8_65.
Fernandez-Basso, Carlos, Karel Gutiérrez-Batista, Roberto Morcillo-Jiménez, Maria-Amparo Vila, and Maria J. Martin-Bautista. 2022. “A Fuzzy-Based Medical System for Pattern Mining in a Distributed Environment: Application to Diagnostic and Co-Morbidity.” Applied Soft Computing 122: 108870. doi:10.1016/j.asoc.2022.108870.
Guan, Zhouyu, Huating Li, Ruhan Liu, Chun Cai, Yuexing Liu, Jiajia Li, Xiangning Wang, et al. 2023. “Artificial Intelligence in Diabetes Management: Advancements, Opportunities, and Challenges.” Cell Reports Medicine 4(10): 101213. doi:10.1016/j.xcrm.2023.101213.
Harding, Jessica L., Meda E. Pavkov, Dianna J. Magliano, Jonathan E. Shaw, and Edward W. Gregg. 2019. “Global Trends in Diabetes Complications: A Review of Current Evidence.” Diabetologia 62(1): 3–16. doi:10.1007/s00125-018-4711-2.
Hashim, Muhammad Jawad. 2022. “The Art of Diabetes Care: Guidelines for a Holistic Approach to Human and Social Factors.” Journal of Yeungnam Medical Science. doi:10.12701/jyms.2022.00577.
Idrees, Thaer, Iris A Castro-Revoredo, Alexandra L Migdal, Emmelin Marie Moreno, and Guillermo E Umpierrez. 2022. “Update on the Management of Diabetes in Long-Term Care Facilities.” BMJ Open Diabetes Research & Care 10(4): e002705. doi:10.1136/bmjdrc-2021-002705.
Jabiyeva, Aynur J. 2022. “Fuzzy Logic Approach to the Amount of Medication Taken During Breathing with an Inhalator.” In , 270–77. doi:10.1007/978-3-030-92127-9_38.
Johnson-Mann, Crystal N., Tyler J. Loftus, and Azra Bihorac. 2021. “Equity and Artificial Intelligence in Surgical Care.” JAMA Surgery 156(6): 509. doi:10.1001/jamasurg.2020.7208.
Li, Shengli, and Cuiping Wei. 2020. “Hesitant Fuzzy Linguistic Correlation Coefficient and Its Applications in Group Decision Making.” International Journal of Fuzzy Systems 22(6): 1748–59. doi:10.1007/s40815-020-00876-z.
Li, Yarong, Pengjiang Qian, Shuihua Wang, and Shitong Wang. 2023. “Novel Multi-View Takagi–Sugeno–Kang Fuzzy System for Epilepsy EEG Detection.” Journal of Ambient Intelligence and Humanized Computing 14(5): 5625–45. doi:10.1007/s12652-021-03189-7.
Li, Yuanyuan, Lei Peng, and Wei Gu. 2023. “The Published Trend of Studies on COVID-19 and Diabetes: Bibliometric Analysis.” Frontiers in Endocrinology 14. doi:10.3389/fendo.2023.1248676.
Liu, Peide, Abbas Ali, Noor Rehman, and Syed Inayat Ali Shah. 2020. “Another View on Intuitionistic Fuzzy Preference Relation-Based Aggregation Operators and Their Applications.” International Journal of Fuzzy Systems 22(6): 1786–1800. doi:10.1007/s40815-020-00882-1.
Liu, Yihao, Xin Ning, Luyao Zhang, Jianyan Long, Ruiming Liang, Sui Peng, Haibo Wang, et al. 2022. “Prevalence of Long-Term Complications in Inpatients with Diabetes Mellitus in China: A Nationwide Tertiary Hospital-Based Study.” BMJ Open Diabetes Research & Care 10(3): e002720. doi:10.1136/bmjdrc-2021-002720.
Loftus, Tyler J., Benjamin Shickel, Tezcan Ozrazgat-Baslanti, Yuanfang Ren, Benjamin S. Glicksberg, Jie Cao, Karandeep Singh, et al. 2022. “Artificial Intelligence-Enabled Decision Support in Nephrology.” Nature Reviews Nephrology 18(7): 452–65. doi:10.1038/s41581-022-00562-3.
Madhu, S. V. 2020. “Post COVID-19 Diabetes Care—Lessons and Challenges.” International Journal of Diabetes in Developing Countries 40(2): 155–57. doi:10.1007/s13410-020-00831-6.
Mahat, Roshan Kumar, Ashwini Manish Jantikar, Vedika Rathore, and Suchismita Panda. 2024. “Circulating Asprosin Levels in Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis.” Clinical Epidemiology and Global Health 25: 101502. doi:10.1016/j.cegh.2023.101502.
Mavani, Nidhi Rajesh, Jarinah Mohd Ali, M.A. Hussain, Norliza Abd. Rahman, and Haslaniza Hashim. 2024. “Determining Food Safety in Canned Food Using Fuzzy Logic Based on Sulphur Dioxide, Benzoic Acid and Sorbic Acid Concentration.” Heliyon 10(4): e26273. doi:10.1016/j.heliyon.2024.e26273.
Mosavi, Amir H., Ardashir Mohammadzadeh, Sakthivel Rathinasamy, Chunwei Zhang, Uwe Reuter, Kovacs Levente, and Hojjat Adeli. 2022. “Deep Learning Fuzzy Immersion and Invariance Control for Type-I Diabetes.” Computers in Biology and Medicine 149: 105975. doi:10.1016/j.compbiomed.2022.105975.
Pintanel, Alice, Graçaliz Dimuro, Eduardo Borges, Giancarlo Lucca, and Camila Barcelos. 2023. “Fuzzy Logic for Diabetes Predictions: A Literature Review.” In Proceedings of the 25th International Conference on Enterprise Information Systems, SCITEPRESS - Science and Technology Publications, 476–83. doi:10.5220/0011851500003467.
Subasi, Abdulhamit. 2020. “Machine Learning Techniques.” In Practical Machine Learning for Data Analysis Using Python, Elsevier, 91–202. doi:10.1016/B978-0-12-821379-7.00003-5.
Tao, Ping, Ching-Wen Chien, Chao Liu, Jinghang Zheng, Dongping Sun, Jibin Zeng, Qunli Song, et al. 2024. “Diabetes Mellitus Is a Risk Factor for Incident Chronic Kidney Disease: A Nationwide Cohort Study.” Heliyon 10(7): e28780. doi:10.1016/j.heliyon.2024.e28780.
Thakkar, Harshil, Vaishnavi Shah, Hiteshri Yagnik, and Manan Shah. 2021. “Comparative Anatomization of Data Mining and Fuzzy Logic Techniques Used in Diabetes Prognosis.” Clinical eHealth 4: 12–23. doi:10.1016/j.ceh.2020.11.001.
Topol, Eric J. 2019. “High-Performance Medicine: The Convergence of Human and Artificial Intelligence.” Nature Medicine 25(1): 44–56. doi:10.1038/s41591-018-0300-7.
Xue, Wei, Ta Zhou, and Jing Cai. 2022. “Horizontal Progressive and Longitudinal Leapfrogging Fuzzy Classification with Feature Activity Adjustment.” Applied Soft Computing 119: 108511. doi:10.1016/j.asoc.2022.108511.
Zhang, Jialu, Xia Wu, and Ruhua Lu. 2020. “Decision Analysis Methods Combining Quantitative Logic and Fuzzy Soft Sets.” International Journal of Fuzzy Systems 22(6): 1801–14. doi:10.1007/s40815-020-00899-6.
Zhang, Yuanpeng, Xiangzhe Li, Junqing Zhu, Chunying Wu, and Qinfeng Wu. 2019. “Epileptic EEG Signals Recognition Using a Deep View-Reduction TSK Fuzzy System With High Interpretability.” IEEE Access 7: 137344–54. doi:10.1109/ACCESS.2019.2942641.
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