AHP based Classification Algorithm Selection for Clinical Decision Support System Development

Abstract

Supervised classification algorithms have become very popular because of their potential application in developing intelligent data analytic software. These algorithms are known to be sensitive to the characteristic and structure of input datasets, therefore, researchers use different algorithm selection methods to select the most suitable classification algorithm for specific dataset. These methods do not consider the uncertainty about input dataset, and relative importance of different performance measurements (such as speed, accuracy, and memory usage) in the target application domain. Therefore, these methods are not appropriate for software development. This is especially true in medical field where various high dimensional noisy data might be used with the software. Hence, software developers need to select one supervised classification algorithm that has the highest potential to provide good performance in wide variety of datasets. In this regard, an Analytic Hierarchy Process (AHP) based meta-learning algorithm is proposed to identify the most suitable supervised classification algorithm for developing clinical decision support system (CDSS). The results from ten publicly available medical datasets indicate that Support Vector Machine (SVM) has the highest potential to perform well on variety of medical datasets.