English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

The International Classification of Functioning, Disability, and Health forChildren and Youth (ICF-CY) is a scaffold for designating and systematizingdata on functioning and disability. It offers a standard semantic and atheoretical foundation for the demarcation and extent of wellbeing andinfirmity. The multidimensional layout of ICF-CY comprehends a plethora ofinformation with about 1400 categories making it difficult to analyze. Ourresearch proposes a predictive model that classify self-care problems onSelf-Care Activities Dataset based on the ICF- CY. The data used in this studyresides 206 attributes of 70 children with motor and physical disability. Ourstudy implements, compare and analyze Random Forest, Support vector machine,Naive Bayes, Hoeffding tree, and Lazy locally weighted learning usingtwo-tailed T-test at 95% confidence interval. Boruta algorithm involved in thestudy minimizes the data dimensionality to advocate the minimal-optimal set ofpredictors. Random forest gave the best classification accuracy of 84.75%; rootmean squared error of 0.18 and receiver operating characteristic of 0.99.Predictive analytics can simplify the usage of ICF-CY by automating theclassification process of disability, functioning, and health.

Classification of Functioning, Disability, and Health for Children and Youth: ICF-CY Self Care (SCADI Dataset) Using Predictive Analytics