학술논문

Feature Sequential Selection Process for Predicting Disease Glomerulonephritis
Document Type
Conference
Source
2022 International Conference on Computer Communication and Informatics (ICCCI) Computer Communication and Informatics (ICCCI), 2022 International Conference on. :01-04 Jan, 2022
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Signal Processing and Analysis
Measurement units
Redundancy
Bladder
Feature extraction
Data mining
Informatics
Kidney
Membranous Nephropathy
Machine Learning
SVM
DT
Ensemble Method
Language
Abstract
Kidneys are important to have beneficial body.They are basically answerable for separating side-effects,abundance water and various debasements out of the blood.Toxins are saved within inside the bladder after which eliminated all through urination. Glomerulonephritis is one of the difficult nephrotic disorder that happens with inside the transplanted kidneys. The severity of the disorder may be anticipated with the aid of using the usage of the clinical data. Classification results had been procured the usage of 3 exclusive overall performance criteria using three different performance criteria(Kappa, Area beneath the ROC curve).Data Mining will yield great outcomes when applied with the tools and procedures.Diagnosing methods can be improved by selecting various attributes and features. Redundancy of data continuously increases day to day. To avoid such redundancy sequential selection method is used. Feature selection method focuses on input space but whereas the sequential method focuses on reduced data consumption. To reduce the data dimensionality feature selection procedure is used.To reduce data dimensionality and provide a high accuracy the proposed method is used.