Multivariate-Stepwise Gaussian Classifier (MSGC): A New Classification Algorithm Tested Over Real Disease Data Sets

Abstract

In data mining, classification is the process of assigning one amongst previously known classes to a new observation. Mathematical algorithms are intensively used for classification. In these, a generalization is inferred from the data, so as to classify new cases, or individuals. The algorithm may misclassify an individual if the inference machine is not able to sufficiently discriminate it. Therefore, it is necessary to go further into the analysis of the information provided by the individual, until it can be sufficiently identified as belonging to a class. This chapter developed this idea for the improvement of a certain class of classifiers, using medical data sets to validate the new algorithm proposed here: The Multivariate-Stepwise Gaussian Classifier (MSGC). The results showed that MSGC is at least as competitive as the Gaussian Maximum Likelihood Classifier. MSGC attained the greatest accuracy rate in two of the data sets, and obtained identical results in the two remaining data sets. Concerning medical applications, once a classification method has been successfully validated considering a particular scope of data, the recommendable would be its use for the best diagnosis. Meanwhile, other algorithms could be tested until they proved to be effective enough to be put into practice.