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

被引:116
作者
Polat, Huseyin [1 ]
Mehr, Homay Danaei [1 ]
Cetin, Aydin [1 ]
机构
[1] Gazi Univ, Fac Technol, Dept Comp Engn, TR-06500 Ankara, Turkey
关键词
Feature selection; Support vector machine; Chronic kidney disease; Machine learning; GENETIC ALGORITHM; BREAST-CANCER; CLASSIFICATION; PREDICTION;
D O I
10.1007/s10916-017-0703-x
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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.
引用
收藏
页数:11
相关论文
共 49 条
  • [1] Support vector machines combined with feature selection for breast cancer diagnosis
    Akay, Mehmet Fatih
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (02) : 3240 - 3247
  • [2] Akbarisanto R, 2016, 2016 4TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY (ICOICT)
  • [3] [Anonymous], 2011, International Journal on Computer Science and Engineering
  • [4] [Anonymous], P 27 INT FLOR ART IN
  • [5] Center for Applied Internet Data Analysis, 2016, UCSD NETW TEL COD RE
  • [6] SVM-based computer-aided diagnosis of the Alzheimer's disease using t-test NMSE feature selection with feature correlation weighting
    Chaves, R.
    Ramirez, J.
    Gorriz, J. M.
    Lopez, M.
    Salas-Gonzalez, D.
    Alvarez, I.
    Segovia, F.
    [J]. NEUROSCIENCE LETTERS, 2009, 461 (03) : 293 - 297
  • [7] Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models
    Chen, Zewei
    Zhang, Xin
    Zhang, Zhuoyong
    [J]. INTERNATIONAL UROLOGY AND NEPHROLOGY, 2016, 48 (12) : 2069 - 2075
  • [8] Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods
    Cho, Baek Hwan
    Yu, Hwanjo
    Kim, Kwang-Won
    Kim, Tae Hyun
    Kim, In Young
    Kim, Sun I.
    [J]. ARTIFICIAL INTELLIGENCE IN MEDICINE, 2008, 42 (01) : 37 - 53
  • [9] Cosovic M, 2015, PERFORM EVALUATION, DOI [10.1109/DINWC.2015.7054228, DOI 10.1109/DINWC.2015.7054228]
  • [10] Davis C, 2016, CREATININE BLOOD TES