Comparison of Computational Learning Methods on a Diagnostic Cytological Application
Konstantinos Koutroumbas, Γεώργιος Παλιούρας, Vangelis Karkaletsis · 2001
ABSTRACT: In this paper we perform a comparative evaluation of four different computational learning methods on a problem of diagnostic cytology and more specifically on the classification of gastric cells. The methods considered are: Decision Tree Induction, Boosted Decision Trees, Naive Bayesian Classifier, and Radial Basis Function Neural Networks. The performance of each method was assessed on unseen data. Our aim was not to evaluate the quality of the algorithms as such, but to examine which of them are suitable for the specific medical diagnosis task, in order to provide a reliable diagnostic tool to the doctors involved in the area. We compare the performance of the four methods and discuss the results taking into account the characteristics of the methods and the task examined. The dataset that was used in this paper is publicly available, facilitating reproducibility of the results and providing a basis of comparison for future work.