Pattern classification with imbalanced and multiclass data for the prediction of albendazole adverse event outcomes

Abstract

Class imbalance problem is one of the important problems for classification studies in data mining. In this study, a comparative analysis of some sampling methods was performed based on the evaluation of four classification algorithms for the prediction of albendazole adverse events outcomes. Albendazole is one of the main medications used for the treatment of a variety of parasitic worm infestations. The dataset was created from the public release of the FDA's FAERS database. Four sampling algorithms were used to analyze the dataset and their performance was evaluated by using four classifiers. Among the algorithms, ID3 with resample algorithm has higher accuracy results than the others after the application of sampling methods. This study supported that sampling methods are capable to improve the performance of learning algorithms. (C) 2016 The Authors. Published by Elsevier B.V.