Feature Extraction Techniques for Chronic Kidney Disease Identification

Abstract

Chronic Kidney Disease (CKD) is one of the dangerous diseases around the world. Early recognition and appropriate administration are requested for enlarging survivability. According to the UCI informational index, there are 24 qualities for anticipating CKD or non-CKD. At any rate there are 16 qualities need obsessive examinations including more assets, cash, time, and vulnerabilities. The goal of this work is to investigate whether we can anticipate CKD or non-CKD with sensible precision utilizing less number of features. An Intellectual framework advancement approach has been utilized in this investigation. Essential feature determination system to find reduced features that clarify the informational index is introduced. Two insightful paired grouping methods have been received for the legitimacy of the reduced list of capabilities. As recommended from our outcomes, we may more focus on those decreased features for recognizing CKD and along these lines lessens vulnerability, spares time, and reduced costs. The proposed technique uses less features for CKD prediction.

Intelligent Diagnosis of Obstetric Diseases Using HGS-AOA Based Extreme Learning Machine

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Tracking and Extracting Action Trajectory of Athlete Based on Hierarchical Features

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