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
Genomics Inform. 2018;16(4):e31  Published online 2018 Dec 28     DOI:
Citations to this article as recorded by Crossref logo
Machine learning-aided risk prediction for metabolic syndrome based on 3 years study
Haizhen Yang, Baoxian Yu, Ping OUYang, Xiaoxi Li, Xiaoying Lai, Guishan Zhang, Han Zhang
Scientific Reports.2022;[Epub]     CrossRef
Development and Validation of a Simple Risk Model for Predicting Metabolic Syndrome (MetS) in Midlife: A Cohort Study
Musa S Ibrahim, Dong Pang, Gurch Randhawa, Yannis Pappas
Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2022; Volume 15: 1051.     CrossRef
Prediction of metabolic and pre-metabolic syndromes using machine learning models with anthropometric, lifestyle, and biochemical factors from a middle-aged population in Korea
Junho Kim, Sujeong Mun, Siwoo Lee, Kyoungsik Jeong, Younghwa Baek
BMC Public Health.2022;[Epub]     CrossRef
Single-nucleotide polymorphisms in medical nutritional weight loss: Challenges and future directions
Moxi Chen, Wei Chen
Journal of Translational Internal Medicine.2022; 10(1): 1.     CrossRef
Building a model for predicting metabolic syndrome using artificial intelligence based on an investigation of whole-genome sequencing
Nai-Wei Hsu, Kai-Chen Chou, Yu-Ting Tina Wang, Chung-Lieh Hung, Chien-Feng Kuo, Shin-Yi Tsai
Journal of Translational Medicine.2022;[Epub]     CrossRef
Prediction of metabolic syndrome: A machine learning approach to help primary prevention
Leonardo Daniel Tavares, Andre Manoel, Thiago Henrique Rizzi Donato, Fernando Cesena, Carlos André Minanni, Nea Miwa Kashiwagi, Lívia Paiva da Silva, Edson Amaro, Claudia Szlejf
Diabetes Research and Clinical Practice.2022; 191: 110047.     CrossRef
Simple Method to Predict Insulin Resistance in Children Aged 6–12 Years by Using Machine Learning
Qian Zhang, Nai-jun Wan
Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2022; Volume 15: 2963.     CrossRef
Metabolic Syndrome Prediction Models Using Machine Learning and Sasang Constitution Type
Ji-Eun Park, Sujeong Mun, Siwoo Lee, Keturah R. Faurot
Evidence-Based Complementary and Alternative Medicine.2021; 2021: 1.     CrossRef
Machine and Deep Learning Applied to Predict Metabolic Syndrome without a Blood Screening
Guadalupe O. Gutiérrez-Esparza, Tania A. Ramírez-delReal, Mireya Martínez-García, Oscar Infante Vázquez, Maite Vallejo, José Hernández-Torruco
Applied Sciences.2021; 11(10): 4334.     CrossRef
Classification and Prediction on the Effects of Nutritional Intake on Overweight/Obesity, Dyslipidemia, Hypertension and Type 2 Diabetes Mellitus Using Deep Learning Model: 4–7th Korea National Health and Nutrition Examination Survey
Hyerim Kim, Dong Hoon Lim, Yoona Kim
International Journal of Environmental Research and Public Health.2021; 18(11): 5597.     CrossRef
Cardiometabolic risk prediction algorithms for young people with psychosis: a systematic review and exploratory analysis
B. I. Perry, R. Upthegrove, O. Crawford, S. Jang, E. Lau, I. McGill, E. Carver, P. B. Jones, G. M. Khandaker
Acta Psychiatrica Scandinavica.2020; 142(3): 215.     CrossRef
Genetic markers and continuity of healthy metabolic status: Tehran cardio-metabolic genetic study (TCGS)
Omid Gharooi Ahangar, Niloufar Javanrouh, Maryam S. Daneshpour, Maryam Barzin, Majid Valizadeh, Fereidoun Azizi, Farhad Hosseinpanah
Scientific Reports.2020;[Epub]     CrossRef
Identification of Metabolic Syndrome Based on Anthropometric, Blood and Spirometric Risk Factors Using Machine Learning
Sang Yeob Kim, Gyeong Hee Nam, Byeong Mun Heo
Applied Sciences.2020; 10(21): 7741.     CrossRef
Development and internal validation of risk prediction model of metabolic syndrome in oil workers
Jie Wang, Chao Li, Jing Li, Sheng Qin, Chunlei Liu, Jiaojiao Wang, Zhe Chen, Jianhui Wu, Guoli Wang
BMC Public Health.2020;[Epub]     CrossRef