Abstract
Medical Family Tree can provide a branch-by-branch indication of the types of diseases that have been present in a family’s past. Some of these diseases may be genetic in nature. By exploring a Medical Family Tree we can become more aware of any genetic factors that may put us at risk of developing genetically-linked diseases. The main purpose of this paper is to present a proposal on a study to explore the medical family data using visual data mining techniques. This article seeks to enable reader to basically understand how and why this type of research is being conducted and how it can be used to help medical practitioners in understanding family health and condition based on information gathered for family medical tree. Initial investigation suggest that visual data mining has huge potentials as it can visually help a lot people such as health practitioners, therapist, clinicians, social workers and others in various fields to understand the patient’s family medical history and to look for recurring patterns of illness and behaviour.
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References
Gill, S.S.: Evolutionary Computing for Cancer Genetics Risk Assessment, MSc IMM dissertation, MACS, Heriot Watt University (2003)
Kimball, R., Mer, R.: The Data Webhouse Toolkit: Building the Web-Enabled Data Warehouse. John Wiley & Sons (2000)
Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery: An overview. In: Advances in Knowledge Discovery and Data Mining, ch. 1, pp. 1–34. AAAI Press, The MIT Press (1996)
Mitchell, T.M.: Machine Learning. McGraw-Hill (1997)
Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning: Data Mining, Inference and Prediction, 2nd edn. Springer (2009)
Berthold, M.R., Dill, F., Kötter, T., Thiel, K.: Supporting creativity: Towards associative discovery of new insights. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 14–25. Springer, Heidelberg (2008)
Fry, B.: Visualizing Data: Exploring and Explaining Data with the Processing Environment. O’Reilly Media (2007)
Kohavi, R.: Data Mining and Visualization. In: Sixth Annual Symposium on Frontiers of Engineering, pp. 30–40. National Academy Press (2001)
Keim, D.A.: Information Visualization and Visual Data Mining. IEEE Transactions on Visualization and Computer Graphics 8(1), 1–8 (2002)
Pandey, B., Mishra, R.B.: Knowledge and intelligent computing system in medicine. Computers in Biology and Medicine 39(3), 215–230 (2009)
Bellazzi, R., Zupan, B.: Predictive data mining in clinical medicine: current issues and guidelines. International Journal of Medical Informatics 77(2), 81–97 (2008)
Souza, J., Matwin, S., Japkowicz, N.: Evaluating data mining models: A pattern language. Presented at PLoP 2002 (2002), http://jerry.cs.uiuc.edu/~plop/plop2002/final/PLoP2002_jtsouza0_1.pdf (retrieved February 18, 2012)
Zainon, W.M.N.W., Talib, A.Z., Belaton, B.: A New Framework for Phylogenetic Tree Visualization. In: Badioze Zaman, H., Robinson, P., Petrou, M., Olivier, P., Shih, T.K., Velastin, S., Nyström, I. (eds.) IVIC 2011, Part II. LNCS, vol. 7067, pp. 348–359. Springer, Heidelberg (2011)
Zainon, W.M.N.W., Talib, A.Z., Belaton, B.: Display-based Approaches for Phylogenetic Tree Visualization. In: Proceedings of the 2nd International Conference on Distributed Frameworks and Applications (DFmA 2010), Yogyakarta, Indonesia, pp. 53–59 (2010) ISBN: 978-602-9747-9-0-4
Altshuler, D., Clark, A.G.: Harvesting Medical Information from the Human Family Tree. Science 307(5712), 1052–1053 (2005)
Delen, D.: Analysis of cancer data: a data mining approach. Expert Systems 26(1), 100–112 (2009)
Kreuseler, M., Schumann, H.: A Flexible Approach for Visual Data Mining. IEEE Transactions on Visualization and Computer Graphics 8(1), 39–51 (2002)
Cornelissen, B., Holten, D., Zaidman, A., Moonen, L., van Wijk, J.J., van Deursen, A.: Understanding Execution Traces Using Massive Sequence and Circular Bundle Views. In: Proceedings of the 15th International Conference on Program Comprehension (ICPC), pp. 49–58. IEEE Computer Society (2007)
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Wan Zainon, W.M.N., Talib, A.Z. (2013). Exploring Medical Family Tree Data Using Visual Data Mining. In: Zaman, H.B., Robinson, P., Olivier, P., Shih, T.K., Velastin, S. (eds) Advances in Visual Informatics. IVIC 2013. Lecture Notes in Computer Science, vol 8237. Springer, Cham. https://doi.org/10.1007/978-3-319-02958-0_39
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DOI: https://doi.org/10.1007/978-3-319-02958-0_39
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