Abstract
Due to the advancement of information technology and its incorporation in various health applications, a huge amount of medical data is being produced continuously. Consequently, efficient techniques are required to analyse such large datasets and extract meaningful information as well as knowledge. Disease diagnosis is an important application domain of data mining techniques and can be resembled with the anomaly detection which is one of the primary tasks of data mining research. In past decades, heart disease caused the maximum death all over the world. As a result, heart disease diagnosis is a challenge for both data mining and health care communities. In this paper, co-clustering is introduced as a powerful data analysis tool to diagnose heart disease and extract the underlying data pattern of the datasets. The performance of the proposed method is evaluated using Cleveland Clinic Foundation Heart Disease dataset against other existing clustering based anomaly detection techniques. Experimental results reflect not only better accuracy but also meaningful information about the dataset which is helpful for further analysis of heart disease diagnosis.
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© 2015 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Ahmed, M., Mahmood, A.N., Maher, M.J. (2015). Heart Disease Diagnosis Using Co-clustering. In: Jung, J., Badica, C., Kiss, A. (eds) Scalable Information Systems. INFOSCALE 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 139. Springer, Cham. https://doi.org/10.1007/978-3-319-16868-5_6
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DOI: https://doi.org/10.1007/978-3-319-16868-5_6
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