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| Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population |
| Eun Kyung Choe, Hwanseok Rhee, Seungjae Lee, Eunsoon Shin, Seung-Won Oh, Jong-Eun Lee, Seung Ho Choi |
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Genomics Inform. 2018;16(4):e31 Published online December 28, 2018 DOI: https://doi.org/10.5808/GI.2018.16.4.e31 |
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Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population Genomics & Informatics. 2018;16(4):e31  Prediction of metabolic and pre-metabolic syndromes using machine learning models with anthropometric, lifestyle, and biochemical factors from a middle-aged population in Korea BMC Public Health. 2022;22(1): Predicting the Occurrence of Metabolic Syndrome Using Machine Learning Models Computation. 2023;11(9):170 Risk prediction of diabetic nephropathy using machine learning techniques: A pilot study with secondary data Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2021;15(5):102263 Is handling unbalanced datasets for machine learning uplifts system performance?: A case of diabetic prediction Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2022;16(9):102609 Prediction of Metabolic Syndrome in a Mexican Population Applying Machine Learning Algorithms Symmetry. 2020;12(4):581 Prediction and Analysis of Machine Learning Models for Efficient Routing Protocol in VANET using Feature Information . 2023; Carbon price prediction using multiple hybrid machine learning models optimized by genetic algorithm Journal of Environmental Management. 2023;342:118061 Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models . 2021; Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models . 2021; |
