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

E-Health / Health Services Research and New Models of Care (513) Clinical Information and Decision Making (1397) Consumer & Patient Education and Shared-Decision Making (673) Participatory Medicine & E-Patients (770) Decision Support for Health Professionals (1179) Machine Learning (1452) Prevention and Health Promotion (978)

Published on in Vol 22, No 6 (2020): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18585, first published .
Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach

Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach

Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach

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 ;   Shiyng-Yu Lin1, 2 Author Orcid Image ;   Jenny L Wu1, 2 Author Orcid Image ;   Shy-Shin Chang1, 2 Author Orcid Image
Article Authors Cited by (23) Tweetations (7) Metrics

Journals

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  2. Latif J, Xiao C, Tu S, Rehman S, Imran A, Bilal A. Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review. IEEE Access 2020;8:150489 View
  3. 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
  4. Kaur H, Ahsaan S, Alankar B, Chang V. A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets. Information Systems Frontiers 2021;23(6):1417 View
  5. Calisto F, Santiago C, Nunes N, Nascimento J. Introduction of human-centric AI assistant to aid radiologists for multimodal breast image classification. International Journal of Human-Computer Studies 2021;150:102607 View
  6. 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
  7. Yu C, Chang S, Chang T, Wu J, Lin Y, Chien H, Chen R. A COVID-19 Pandemic Artificial Intelligence–Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study. Journal of Medical Internet Research 2021;23(5):e27806 View
  8. Enriquez J, Chu Y, Pudakalakatti S, Hsieh K, Salmon D, Dutta P, Millward N, Lurie E, Millward S, McAllister F, Maitra A, Sen S, Killary A, Zhang J, Jiang X, Bhattacharya P, Shams S. Hyperpolarized Magnetic Resonance and Artificial Intelligence: Frontiers of Imaging in Pancreatic Cancer. JMIR Medical Informatics 2021;9(6):e26601 View
  9. 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
  10. Ahmed A, Ashour O, Ali H, Firouz M. An integrated optimization and machine learning approach to predict the admission status of emergency patients. Expert Systems with Applications 2022;202:117314 View
  11. De D, Nayak T, Chowdhury S, Dhal P. Insights of Host Physiological Parameters and Gut Microbiome of Indian Type 2 Diabetic Patients Visualized via Metagenomics and Machine Learning Approaches. Frontiers in Microbiology 2022;13 View
  12. Alfian G, Syafrudin M, Fahrurrozi I, Fitriyani N, Atmaji F, Widodo T, Bahiyah N, Benes F, Rhee J. Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method. Computers 2022;11(9):136 View
  13. Haruna U, Musa S, Manirambona E, Lucero-Prisno D, Sarría-Santamera A. Monkeypox: Is the world ready for another pandemic?. Frontiers in Public Health 2022;10 View
  14. Chan Y, Chang S, Wu J, Wang S, Yu C. Association between liver stiffness measurement by transient elastography and chronic kidney disease. Medicine 2022;101(4):e28658 View
  15. Houssein E, Hosney M, Emam M, Younis E, Ali A, Mohamed W. Soft computing techniques for biomedical data analysis: open issues and challenges. Artificial Intelligence Review 2023;56(S2):2599 View
  16. Ahmed A, Al-Maamari M, Firouz M, Delen D. An Adaptive Simulated Annealing-Based Machine Learning Approach for Developing an E-Triage Tool for Hospital Emergency Operations. Information Systems Frontiers 2023 View
  17. Jaiswal V, Suman P, Bisen D. An improved ensembling techniques for prediction of breast cancer tissues. Multimedia Tools and Applications 2023;83(11):31975 View
  18. 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
  19. 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

Books/Policy Documents

  1. Rojo J, Moguel E, Fonseca C, Lopes M, Garcia-Alonso J, Hernandez J. Gerontechnology III. View
  2. Prasad Reddy T, Vydeki . Fourth Congress on Intelligent Systems. View