Performance Analysis of Some Competent Learners on Medical Data

Abstract

Research in disease diagnosis is a challenging task due to inconsistent, class imbalance, conflicting, and the high dimensionality of medical data sets. The excellent features of each data set play an important role in improving performance of classifiers that may follow either iterative or non-iterative approaches. In the present study, a comparative study is carried out to show the performance of iterative and non-iterative classifiers in combination with genetic algorithm (GA)-based feature selection approach over some widely used medical data sets. The experiment assists to identify the clinical data sets for which feature reduction is necessary for improving performance of classifiers. For iterative approaches, two popular classifiers, namely C4.5 and RIPPER, are chosen, whereas k-NN and naïve Bayes are taken as non-iterative learners. Fourteen real-world medical domain data sets are selected from the University of California, Irvine (UCI Repository) for conducting experiment over the learners.