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| Development of Various Diabetes Prediction Models Using Machine Learning Techniques |
| Juyoung Shin, Jaewon Kim, Chanjung Lee, Joon Young Yoon, Seyeon Kim, Seungjae Song, Hun-Sung Kim |
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Diabetes Metab J. 2022;46(4):650-657. Published online March 11, 2022 DOI: https://doi.org/10.4093/dmj.2021.0115 |
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Development of Various Diabetes Prediction Models Using Machine Learning Techniques Diabetes & Metabolism Journal. 2022;46(4):650-657  Atmospheric Weather Prediction Using various machine learning Techniques: A Survey 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC). 2019; Diabetes Prediction using Machine Learning Techniques 2019 International Conference on Intelligent Computing and Remote Sensing (ICICRS). 2019; Prediction of Diabetes Using Various Feature Selection and Machine Learning Paradigms Modern Approaches in Machine Learning & Cognitive Science: A Walkthrough. 2022;133-146 Comparative Analysis of Prediction of Coronal Mass Ejections (CME) based on Sunspot Activities Using Various Machine Learning Models 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST). 2019; Class Imbalance Issue in Software Defect Prediction Models by various Machine Learning Techniques: An Empirical Study 2021 8th International Conference on Smart Computing and Communications (ICSCC). 2021; Machine learning techniques for daily solar energy prediction and interpolation using numerical weather models Concurrency and Computation: Practice and Experience. 2015;28(4):1261-1274 A Review on Prediction of Diabetes Using Various Machine Learning Algorithms ECS Transactions. 2022;107(1):5785-5795 A Decision Support System for Diabetes Prediction Using Machine Learning and Deep Learning Techniques 2019 1st International Informatics and Software Engineering Conference (UBMYK). 2019; Efficient Discovery of Data and Prediction of Coma Occurrence using Various Machine Learning Models 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM). 2019; |
