Abstract
The work presents a model construction process which is a combination of the inductive learning based detection of interesting sub- groups, comparative statistical analyses of risk factors for these groups, and expert knowledge interpretation of the results. The induced models describe population subgroups with unproportionately high rate of the disease what might be helpful in the prevention process.
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References
Gamberger, D. and Lavrač, N. (2000) Confirmation rule sets. In Proc. of 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD2000), pp.34–43.
Goldman, L., Garber, A.M., Grover, S.A., Hlatky, M.A. (1996) Cost-effectiveness of assessments and management of risk factors. Journal of American College Cardiology 27:1020–1030.
Maron, D., Ridker, P.M, Pearson A.T. (1998) Risk factors and the prevention of coronary heart disease. In Wayne A.R., Schlant R.C., Fuster V.: HURST’S: The Heart, 1175–1195. McGrawc Hill, NY.
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© 2001 Springer-Verlag Berlin Heidelberg
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Krstačič, G., Gamberger, D., Šmuc, T. (2001). Coronary Heart Disease Patient Models Based on Inductive Machine Learning. In: Quaglini, S., Barahona, P., Andreassen, S. (eds) Artificial Intelligence in Medicine. AIME 2001. Lecture Notes in Computer Science(), vol 2101. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48229-6_15
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DOI: https://doi.org/10.1007/3-540-48229-6_15
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