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Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods

  • Systems-Level Quality Improvement
  • Published:
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Abstract

As Chronic Kidney Disease progresses slowly, early detection and effective treatment are the only cure to reduce the mortality rate. Machine learning techniques are gaining significance in medical diagnosis because of their classification ability with high accuracy rates. The accuracy of classification algorithms depend on the use of correct feature selection algorithms to reduce the dimension of datasets. In this study, Support Vector Machine classification algorithm was used to diagnose Chronic Kidney Disease. To diagnose the Chronic Kidney Disease, two essential types of feature selection methods namely, wrapper and filter approaches were chosen to reduce the dimension of Chronic Kidney Disease dataset. In wrapper approach, classifier subset evaluator with greedy stepwise search engine and wrapper subset evaluator with the Best First search engine were used. In filter approach, correlation feature selection subset evaluator with greedy stepwise search engine and filtered subset evaluator with the Best First search engine were used. The results showed that the Support Vector Machine classifier by using filtered subset evaluator with the Best First search engine feature selection method has higher accuracy rate (98.5%) in the diagnosis of Chronic Kidney Disease compared to other selected methods.

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Abbreviations

CKD:

Chronic Kidney Disease

UCI:

University of California Irvine

SVM:

Support Vector Machine

GA:

Genetic Algorithm

SymmetricUncertAttributesetEval:

Symmetrical uncertainty attribute set evaluator

SVEGA:

Shapely Value Embedded Genetic Algorithm

KNN:

K-nearest Neighbor

GainRatioAttributeEval:

Gain ratio attribute evaluator

PrincipalComponentsAttributeEval:

Principal components attribute evaluator

SIMCA:

Soft Independent Modeling of Class Analogy

AUC:

Area Under the roc Curve

TCMSP:

Traditional Chinese Medicine Syndrome Prediction method

OSAF:

Oscillating Search Algorithm Feature Selection

NotCKD:

Without Chronic Kidney Disease

ClassifierSubsetEval:

Classifier subset evaluator

WrapperSubsetEval:

Wrapper subset evaluator

FilterSubsetEval:

Filtered subset evaluator

CfsSubsetEval:

Correlation feature selection subset evaluator

TP:

True Positive

TN:

True Negative

FP:

False Positive

FN:

False Negative

ROC:

Receiver Operating Characteristic

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Technology, Gazi University, 06500, Teknikokullar, Ankara, Turkey

    Huseyin Polat, Homay Danaei Mehr & Aydin Cetin

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  1. Huseyin Polat
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  2. Homay Danaei Mehr
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Correspondence to Huseyin Polat.

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Polat, H., Danaei Mehr, H. & Cetin, A. Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods. J Med Syst 41, 55 (2017). https://doi.org/10.1007/s10916-017-0703-x

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