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

Machine Learning (1501) eHealth Literacy / Digital Literacy (396) Consumer & Patient Education and Shared-Decision Making (695) Health Education for Children and Adolescents (43)

Published on in Vol 9, No 7 (2021): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30115, first published .
Predicting Writing Styles of Web-Based Materials for Children’s Health Education Using the Selection of Semantic Features: Machine Learning Approach

Predicting Writing Styles of Web-Based Materials for Children’s Health Education Using the Selection of Semantic Features: Machine Learning Approach

Predicting Writing Styles of Web-Based Materials for Children’s Health Education Using the Selection of Semantic Features: Machine Learning Approach

Authors of this article:

Wenxiu Xie1 Author Orcid Image ;   Meng Ji2 Author Orcid Image ;   Yanmeng Liu2 Author Orcid Image ;   Tianyong Hao3 Author Orcid Image ;   Chi-Yin Chow1 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Lee J, Lee S. Identification of Risk Groups for and Factors Affecting Metabolic Syndrome in South Korean Single-Person Households Using Latent Class Analysis and Machine Learning Techniques: Secondary Analysis Study. JMIR Formative Research 2023;7:e42756 View