Effective framework for prediction of disease outcome using medical datasets: clustering and classification

The method of processing two algorithms within a single workflow, and hence the combined method, is called as hybrid computing. We propose a data mining framework comprising of two stages, namely clustering and classification. The first stage employs k-means algorithm on data and generates two clusters, namely cluster-0 and cluster-1. Instances in cluster-0 do not have disease symptoms and cluster-1 consists of instances with disease symptoms. The verification of valid grouping is then carried out by referring to the association of class labels in original datasets. Incorrectly classified instances are removed and remaining instances are used to build the classifier using C4.5 decision-tree algorithm with k-fold cross validation method. The framework was tested using eight datasets from the machine learning repository of the UCI. The proposed framework was evaluated for accuracy, sensitivity and specificity measures. Our framework obtained promising classification accuracy as compared to other methods found in the literature.