Article: Effective framework for prediction of disease outcome using medical datasets: clustering and classification Journal: International Journal of Computational Intelligence Studies (IJCISTUDIES) 2010 Vol.1 No.3 pp.273 - 290 Abstract: 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: B.M. Patil, Ramesh C. Joshi, Durga Toshniwal

Addresses: Department of Electronics and Computer Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India. ' Department of Electronics and Computer Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India. ' Department of Electronics and Computer Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India

Abstract: 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.

Keywords: clustering; classification; effective framework; hybrid computing; disease outcomes; computational intelligence; medical datasets; data mining; machine learning; disease outcome prediction.

DOI: 10.1504/IJCISTUDIES.2010.034890

International Journal of Computational Intelligence Studies, 2010 Vol.1 No.3, pp.273 - 290

Published online: 26 Aug 2010 *

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

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