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

Areas where Artificial Intelligence (AI) & related fields are finding theirapplications are increasing day by day, moving from core areas of computerscience they are finding their applications in various other domains.In recenttimes Machine Learning i.e. a sub-domain of AI has been widely used in order toassist medical experts and doctors in the prediction, diagnosis and prognosisof various diseases and other medical disorders. In this manuscript the authorsapplied various machine learning algorithms to a problem in the domain ofmedical diagnosis and analyzed their efficiency in predicting the results. Theproblem selected for the study is the diagnosis of the Chronic KidneyDisease.The dataset used for the study consists of 400 instances and 24attributes. The authors evaluated 12 classification techniques by applying themto the Chronic Kidney Disease data. In order to calculate efficiency, resultsof the prediction by candidate methods were compared with the actual medicalresults of the subject.The various metrics used for performance evaluation arepredictive accuracy, precision, sensitivity and specificity. The resultsindicate that decision-tree performed best with nearly the accuracy of 98.6%,sensitivity of 0.9720, precision of 1 and specificity of 1.

Performance Based Evaluation of Various Machine Learning Classification Techniques for Chronic Kidney Disease Diagnosis