Case Study: Classification Algorithms Comparison for the Multi-Label Problem of Chronic Pelvic Pain Diagnosing
Vinicius Oliverio, Omero Benedicto Poli‐Neto · 2017
Chronic pelvic pain is a common clinical condition with negative consequences in many aspects of women's life. The clinical presentation is heterogeneous and the involvement of several body systems impairs the identification of the exact etiology of the problem. At the same time, a clinical treatment of good quality depends on the professional and the learning process is slow. The goal of this paper is to compare different classification algorithms for diagnosing the probable cause of the chronic pelvic pain in those women. A multi-label problem modeling technique and an attribute selection algorithm where used to prepare the problem to the comparison of the algorithms Naive Bayes, C4.5, Support Vector Machines, AdaBoost and KNN.