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

Abstract

Background Medical writing styles can have an impact on the understandability of health educational resources. Amid current web-based health information research, there is a dearth of research-based evidence that demonstrates what constitutes the best practice of the development of web-based health resources on children’s health promotion and education. Objective Using authoritative and highly influential web-based children’s health educational resources from the Nemours Foundation, the largest not-for-profit organization promoting children’s health and well-being, we aimed to develop machine learning algorithms to discriminate and predict the writing styles of health educational resources on children versus adult health promotion using a variety of health educational resources aimed at the general public. Methods The selection of natural language features as predicator variables of algorithms went through initial automatic feature selection using ridge classifier, support vector machine, extreme gradient boost tree, and recursive feature elimination followed by revision by education experts. We compared algorithms using the automatically selected (n=19) and linguistically enhanced (n=20) feature sets, using the initial feature set (n=115) as the baseline. Results Using five-fold cross-validation, compared with the baseline (115 features), the Gaussian Naive Bayes model (20 features) achieved statistically higher mean sensitivity (P=.02; 95% CI −0.016 to 0.1929), mean specificity (P=.02; 95% CI −0.016 to 0.199), mean area under the receiver operating characteristic curve (P=.02; 95% CI −0.007 to 0.140), and mean macro F1 (P=.006; 95% CI 0.016-0.167). The statistically improved performance of the final model (20 features) is in contrast to the statistically insignificant changes between the original feature set (n=115) and the automatically selected features (n=19): mean sensitivity (P=.13; 95% CI −0.1699 to 0.0681), mean specificity (P=.10; 95% CI −0.1389 to 0.4017), mean area under the receiver operating characteristic curve (P=.008; 95% CI 0.0059-0.1126), and mean macro F1 (P=.98; 95% CI −0.0555 to 0.0548). This demonstrates the importance and effectiveness of combining automatic feature selection and expert-based linguistic revision to develop the most effective machine learning algorithms from high-dimensional data sets. Conclusions We developed new evaluation tools for the discrimination and prediction of writing styles of web-based health resources for children’s health education and promotion among parents and caregivers of children. User-adaptive automatic assessment of web-based health content holds great promise for distant and remote health education among young readers. Our study leveraged the precision and adaptability of machine learning algorithms and insights from health linguistics to help advance this significant yet understudied area of research.

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

“…In this analysis, variables such as age, BMI, obesity, alcohol consumption, and subjective body shape recognition were commonly derived. This suggests that the factors identified in previous studies as affecting metabolic syndrome in adult single-person households and the factors identified by applying machine learning techniques in this study are consistent with each other [ 30 ]. It is important to actively encourage physical activity to prevent metabolic syndrome [ 39 ].…”

“…This study aimed to identify the factors affecting metabolic syndrome in single-person households using machine learning with large-scale health data from the National Health and Nutrition Examination Survey (NHANES) [ 30 ]. However, few studies have applied machine learning and LCA to identify the factors affecting metabolic syndrome in single-person households [ 23 , 24 , 30 ].…”

“…This study aimed to identify the factors affecting metabolic syndrome in single-person households using machine learning with large-scale health data from the National Health and Nutrition Examination Survey (NHANES) [ 30 ]. However, few studies have applied machine learning and LCA to identify the factors affecting metabolic syndrome in single-person households [ 23 , 24 , 30 ]. The contribution or significance of this study is not finding any exact answer but finding new variables or overlooked parts through basic research or translational research for clinical application.…”