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/

Global: Zendy Plus annual

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

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

One of the preprocessing steps is data cleaning and feature selection in data mining. Feature selection has more efficiency regarding dimensionality reduction, eliminating irrelevant data, improving the accuracy and enhancing the output comprehensibility. This paper utilizes wrapper / hybridfilter based feature selection method for feature selection and extraction from medical dataset. From the extracted information, the individual features are evaluated by calculating a rank value where it helps to choose highly correlated data from the entire dataset. Selected features are classified using the popular C4.5 classifier. To experiment the proposed method, the benchmark dataset is obtained from the UCI repository. It is a famous machine learning repository used by several earlier research works to evaluate the performance of their proposed methods. Finally, the accuracy of the classification method shows that our proposed method outperforms than the existing methods.

Correlation based Ensemble Feature Selection Algorithm for Diagnosis of Diabetics