This paper is in the following e-collection/theme issue:

Machine Learning (1465) Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1185)

Published on in Vol 8, No 3 (2020): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17110, first published .
Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study

Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study

Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study

Authors of this article:

Cheng-Sheng Yu1, 2 Author Orcid Image ;   Yu-Jiun Lin1, 2 Author Orcid Image ;   Chang-Hsien Lin1, 2 Author Orcid Image ;   Sen-Te Wang1, 2 Author Orcid Image ;   Shiyng-Yu Lin1, 2 Author Orcid Image ;   Sanders H Lin1 Author Orcid Image ;   Jenny L Wu1, 2 Author Orcid Image ;   Shy-Shin Chang1, 2 Author Orcid Image
Article Authors Cited by (36) Tweetations (1) Metrics

Journals

  1. Yu C, Lin Y, Lin C, Lin S, Wu J, Chang S. Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach. Journal of Medical Internet Research 2020;22(6):e18585 View
  2. Lin Y, Chen R, Tang J, Yu C, Wu J, Chen L, Chang S. Machine-Learning Monitoring System for Predicting Mortality Among Patients With Noncancer End-Stage Liver Disease: Retrospective Study. JMIR Medical Informatics 2020;8(10):e24305 View
  3. Sheikhtaheri A, Zarkesh M, Moradi R, Kermani F. Prediction of neonatal deaths in NICUs: development and validation of machine learning models. BMC Medical Informatics and Decision Making 2021;21(1) View
  4. Yu C, Chang S, Lin C, Lin Y, Wu J, Chen R. Identify the Characteristics of Metabolic Syndrome and Non-obese Phenotype: Data Visualization and a Machine Learning Approach. Frontiers in Medicine 2021;8 View
  5. Surodina S, Lam C, Grbich S, Milne-Ives M, van Velthoven M, Meinert E. Machine Learning for Risk Group Identification and User Data Collection in a Herpes Simplex Virus Patient Registry: Algorithm Development and Validation Study. JMIRx Med 2021;2(2):e25560 View
  6. Yu C, Chen Y, Chang S, Tang J, Wu J, Lin C. Exploring and predicting mortality among patients with end-stage liver disease without cancer: a machine learning approach. European Journal of Gastroenterology & Hepatology 2021;33(8):1117 View
  7. Jiang X, Yang Z, Wang S, Deng S. “Big Data” Approaches for Prevention of the Metabolic Syndrome. Frontiers in Genetics 2022;13 View
  8. Yang H, Yu B, OUYang P, Li X, Lai X, Zhang G, Zhang H. Machine learning-aided risk prediction for metabolic syndrome based on 3 years study. Scientific Reports 2022;12(1) View
  9. Dritsas E, Trigka M. Machine Learning Techniques for Chronic Kidney Disease Risk Prediction. Big Data and Cognitive Computing 2022;6(3):98 View
  10. Daniel Tavares L, Manoel A, Henrique Rizzi Donato T, Cesena F, André Minanni C, Miwa Kashiwagi N, Paiva da Silva L, Amaro E, Szlejf C. Prediction of metabolic syndrome: A machine learning approach to help primary prevention. Diabetes Research and Clinical Practice 2022;191:110047 View
  11. Barron M, Luna J, Ventura S. Facing Up Fare War: Generating Competitive Price Models With Gene Expression Programming. IEEE Access 2022;10:125298 View
  12. Dritsas E, Trigka M. Lung Cancer Risk Prediction with Machine Learning Models. Big Data and Cognitive Computing 2022;6(4):139 View
  13. Genet Ngcayiya P, Ranchod P, du Preez W. Comparative Performance Analysis of Random Forests against AutoPrognosis for predicting Coronary Heart Disease Risk and Metabolic Syndrome: A Retrospective Cohort Study. MATEC Web of Conferences 2022;370:07005 View
  14. Gautier T, Ziegler L, Gerber M, Campos-Náñez E, Patek S. Artificial intelligence and diabetes technology: A review. Metabolism 2021;124:154872 View
  15. Benmohammed K, Valensi P, Omri N, Al Masry Z, Zerhouni N. Metabolic syndrome screening in adolescents: New scores AI_METS based on artificial intelligence techniques. Nutrition, Metabolism and Cardiovascular Diseases 2022;32(12):2890 View
  16. Hosseini-Esfahani F, Alafchi B, Cheraghi Z, Doosti-Irani A, Mirmiran P, Khalili D, Azizi F. Using Machine Learning Techniques to Predict Factors Contributing to the Incidence of Metabolic Syndrome in Tehran: Cohort Study. JMIR Public Health and Surveillance 2021;7(9):e27304 View
  17. Ganapathy S, K.T. H, Jindal B, Naik P, Nair N. S. Comparison of diagnostic accuracy of models combining the renal biomarkers in predicting renal scarring in pediatric population with vesicoureteral reflux (VUR). Irish Journal of Medical Science (1971 -) 2023;192(5):2521 View
  18. Alsareii S, Shaf A, Ali T, Zafar M, Alamri A, AlAsmari M, Irfan M, Awais M. IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and Adults. Life 2022;12(9):1414 View
  19. Delgado-Gallegos J, Avilés-Rodriguez G, Padilla-Rivas G, De los Ángeles Cosío-León M, Franco-Villareal H, Nieto-Hipólito J, de Dios Sánchez López J, Zuñiga-Violante E, Islas J, Romo-Cardenas G. Application of C5.0 Algorithm for the Assessment of Perceived Stress in Healthcare Professionals Attending COVID-19. Brain Sciences 2023;13(3):513 View
  20. Kim H, Heo J, Lim D, Kim Y. Development of a Metabolic Syndrome Classification and Prediction Model for Koreans Using Deep Learning Technology: The Korea National Health and Nutrition Examination Survey (KNHANES) (2013–2018). Clinical Nutrition Research 2023;12(2):138 View
  21. Ramírez-Mejía M, Qi X, Abenavoli L, Romero-Gómez M, Eslam M, Méndez-Sánchez N. Metabolic dysfunction: The silenced connection with fatty liver disease. Annals of Hepatology 2023;28(6):101138 View
  22. Chiu K, Chen Y, Wang S, Chang T, Wu J, Shih C, Yu C. Exploring the Potential Performance of Fibroscan for Predicting and Evaluating Metabolic Syndrome using a Feature Selected Strategy of Machine Learning. Metabolites 2023;13(7):822 View
  23. Trigka M, Dritsas E. Predicting the Occurrence of Metabolic Syndrome Using Machine Learning Models. Computation 2023;11(9):170 View
  24. Wang Y, Zhang W, Yang Y, Sun J, Wang L. Survival Prediction of Esophageal Squamous Cell Carcinoma Based on the Prognostic Index and Sparrow Search Algorithm-Support Vector Machine. Current Bioinformatics 2023;18(7):598 View
  25. Boitor O, Stoica F, Mihăilă R, Stoica L, Stef L. Automated Machine Learning to Develop Predictive Models of Metabolic Syndrome in Patients with Periodontal Disease. Diagnostics 2023;13(24):3631 View
  26. Lim S, Lim C, Müller-Riemenschneider F, van Dam R, Sim X, Chong M, Chia A. Development and validation of a lifestyle risk index to screen for metabolic syndrome and its components in two multi-ethnic cohorts. Preventive Medicine 2024;179:107821 View
  27. Chang T, Chen Y, Lu H, Wu J, Mak K, Yu C. Specific patterns and potential risk factors to predict 3-year risk of death among non-cancer patients with advanced chronic kidney disease by machine learning. Medicine 2024;103(7):e37112 View
  28. Pawade D, Bakhai D, Admane T, Arya R, Salunke Y, Pawade Y. Evaluating the Performance of Different Machine Learning Models for Metabolic Syndrome Prediction. Procedia Computer Science 2024;235:2932 View
  29. Gümüş A, Açık M, Durmaz S. Health Star Rating of Nonalcoholic, Packaged, and Ready-to-Drink Beverages in Türkiye: A Decision Tree Model Study. Preventive Nutrition and Food Science 2024;29(2):199 View
  30. Shin D. Prediction of metabolic syndrome using machine learning approaches based on genetic and nutritional factors: a 14-year prospective-based cohort study. BMC Medical Genomics 2024;17(1) View
  31. Cheng P, He B, Wu Z, Liu J, Wang J, Yang C, Ma S, Zhang M, Dong X, Li J. Interpreting the Epidemiological Characteristics of HIV-1 in Heterosexually Transmitted Population Based on Molecular Transmission Network in Kunming, Yunnan: A Retrospective Cohort Study. AIDS Research and Human Retroviruses 2024 View
  32. Setiawan K, Kurniawan A, Prasetyo S. Comparative analysis of machine learning decision tree-based models for predicting maternal health risks. Procedia Computer Science 2024;245:57 View

Books/Policy Documents

  1. Zhong K, Liu G. Artificial Intelligence. View
  2. Zhang J, Li D. Metabolic Syndrome. View
  3. Adua E, Afrifa-Yamoah E, Kolog E. All Around Suboptimal Health. View