A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM.
The use of machine learning tools has become widespread in medical diagnosis. The main reason for this is the effective results obtained from classification and diagnosis systems developed to help medical professionals in the diagnosis phase of diseases. The primary objective of this study is to imp...
| Publicado en: | Journal of Medical Systems Vol. 40; no. 5; pp. 1 - 17 |
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| Autor principal: | |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
May2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=115925332&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925332 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: May2016 vid: 40 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925332 115925332 115925332 10.1007/s10916-016-0477-6 115925332 ppf: 1 ppct: 16 formats: fmt: @attributes: type: P tig: atl: A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM. aug: au: Peker, Musa affil: Department of Information Systems Engineering, Faculty of Technology, Mugla Sitki Kocman University, 48000 Mugla Turkey sug: subj: Decision Support Systems, Clinical Disease Diagnosis Artificial Intelligence Disease Classification Heart Diseases Classification Parkinson Disease Classification Liver Diseases Classification Descriptive Statistics Validity Sensitivity and Specificity ROC Curve kappa Statistic Comparative Studies Algorithms Factor Analysis ab: The use of machine learning tools has become widespread in medical diagnosis. The main reason for this is the effective results obtained from classification and diagnosis systems developed to help medical professionals in the diagnosis phase of diseases. The primary objective of this study is to improve the accuracy of classification in medical diagnosis problems. To this end, studies were carried out on 3 different datasets. These datasets are heart disease, Parkinson's disease (PD) and BUPA liver disorders. Key feature of these datasets is that they have a linearly non-separable distribution. A new method entitled k-medoids clustering-based attribute weighting (kmAW) has been proposed as a data preprocessing method. The support vector machine (SVM) was preferred in the classification phase. In the performance evaluation stage, classification accuracy, specificity, sensitivity analysis, f-measure, kappa statistics value and ROC analysis were used. Experimental results showed that the developed hybrid system entitled kmAW + SVM gave better results compared to other methods described in the literature. Consequently, this hybrid intelligent system can be used as a useful medical decision support tool. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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