An application of a new meta-heuristic for optimizing the classification accuracy when analyzing some medical datasets

Medical data mining has recently become one of the most popular topics in the data mining community. This is due to the societal importance of the field and also the particular computational challenges posed in this domain of data mining. However, current medical data mining approaches oftentimes use identical costs or just ignore them for the different cases of classification errors. Thus, their outcome may be unexpected. This paper applies a new meta-heuristic approach, called the Homogeneity-Based Algorithm (or HBA), for optimizing the classification accuracy when analyzing some medical datasets. The HBA first expresses the objective as an optimization problem in terms of the error rates and the associated penalty costs. These costs may be dramatically different in medical applications as the implications of having a false-positive and a false-negative case may be tremendously different. When the HBA is combined with traditional classification algorithms, it enhances their prediction accuracy. It does so by using the concept of homogenous sets. Five medical datasets, obtained from the machine learning data repository at the University of California, Irvine (UCI), USA, were tested. Some computational results indicate that the HBA, when it is combined with traditional methods, can significantly outperform current stand-alone data mining approaches.