Predicting Readability of Health Educational Resources for Children Using Semantic Features
The success of health education resources largely depends on their readability, as the health information can only be understood and accepted by the target readers when the information is uttered with proper reading difficulty. Unlike other populations, children feature limited knowledge and underdeveloped reading comprehension, which poses more challenges for the readability research on health education resources. This research aims to explore the readability prediction of health education resources for children by using semantic features to develop machine learning algorithms. A data-driven method was applied in this research:1000 health education articles were collected from international health organization websites, and they were grouped into resources for kids and resources for non-kids according to their sources. Moreover, 73 semantic features were used to train five machine learning algorithms (decision tree, support vector machine, k-nearest neighbors algorithm, ensemble classifier, and logistic regression). The results showed that the k-nearest neighbors algorithm and ensemble classifier outperformed in terms of area under the operating characteristic curve sensitivity, specificity, and accuracy and achieved good performance in predicting whether the readability of health education resources is suitable for children or not.
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