Author(s): Ajalmar Rego da Rocha Neto, Guilherme de Alencar Barreto
This paper reports results from a comprehensive performance comparison among standalone machine learning algorithms (SVM, MLP and GRNN) and their combinations in ensembles of classifiers when applied to a medical diagnosis problem in the field of orthopedics. All the aforementioned learning strategies, which currently comprises the classification module of the SINPATCO platform, are evaluated according to their ability in discriminating patients as belonging to one out of three categories: normal, disk hernia and spondylolisthesis. Confusion matrices of all learning algorithms are also reported, as well as a study of the effect of diversity in the design of the ensembles. The obtained results clearly indicate that the ensembles of classifiers have better generalization performance than standalone classifiers.