Medical decision support system for extremely imbalanced datasets

Advanced biomedical instruments and data acquisition techniques generate large amount of physiological data. For accurate diagnosis of related pathology, it has become necessary to develop new methods for analyzing and understanding this data. Clinical decision support systems are designed to provide real time guidance to healthcare experts. These are evolving as an alternate strategy to increase the exactness of diagnostic testing. Generalization ability of these systems is governed by the characteristics of dataset used during its development. It is observed that sub pathologies have a much varied ratio of occurrence in the population, making the dataset extremely imbalanced. This problem can be resolved at both levels i.e. at data level as well as algorithmic level. This work proposes a synthetic sampling technique to balance dataset along with Modified Particle Swarm Optimization (M-PSO) technique. A comparative study of multiclass support vector machine (SVM) classifier optimization algorithm based on grid selection (GSVM), hybrid feature selection (SVMFS), genetic algorithm (GA) and M-PSO is presented in this work. Empirical analysis of five machine learning algorithms demonstrate that M-PSO statistically outperforms the others.