title: Breast Cancer Detection with Reduced Feature Set.
abstract: This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN), artificial neural network (ANN), radial basis function neural network (RBFNN), and support vector machine (SVM). The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations) and partitioning (20%-40%) methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden's index, discriminant power, and the receiver operating characteristic (ROC) curve with its criterion values including area under curve (AUC) and 95% confidential interval (CI). This represents an improvement in diagnostic decision support system, while reducing computational complexity.
authors.affiliation: Department of Electrical and Electronics, Piri Reis University, 34940 Istanbul, Turkey.
authors.forename: Ahmet
authors.identifier: 0000-0003-4236-3646
authors.initials: A
authors.lastname: Mert
authors.affiliation: Department of Electrical and Electronics, Istanbul University, 34320 Istanbul, Turkey.
authors.forename: Niyazi
authors.initials: N
authors.lastname: Kılıç
authors.affiliation: Department of Electrical and Electronics, Piri Reis University, 34940 Istanbul, Turkey.
authors.forename: Erdem
authors.initials: E
authors.lastname: Bilgili
authors.affiliation: Department of Electrical and Electronics, Istanbul University, 34320 Istanbul, Turkey.
authors.forename: Aydin
authors.identifier: 0000-0001-8894-5794
authors.initials: A
authors.lastname: Akan
mesh_terms: D044963:Biopsy, Fine-Needle; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology; D019295:Computational Biology; D016208:Databases, Factual; D020000:Decision Support Systems, Clinical; D005260:Female; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods