title: A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM.
abstract: 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.
authors.affiliation: Department of Information Systems Engineering, Faculty of Technology, Mugla Sitki Kocman University, 48000, Mugla, Turkey. musa@mu.edu.tr.
authors.forename: Musa
authors.identifier: http://orcid.org/0000-0002-6495-9187
authors.initials: M
authors.lastname: Peker
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005103:Expert Systems; D006331:Heart Diseases / Q000175:diagnosis; D006801:Humans; D008107:Liver Diseases / Q000175:diagnosis; D010300:Parkinson Disease / Q000175:diagnosis; D012372:ROC Curve; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: Decision support system; Hybrid classification method; Medical diagnosis; Support vector machine; k-medoids clustering based attribute weighting