Abstract
The inductive learning algorithms are the very attractive methods generating hierarchical classifiers. They generate hypothesis of the target concept on the base on the set of labeled examples. This paper presents some of the decision tree induction methods, boosting concept and their usefulness for diagnosis of the type of hypertension (essential hypertension and five type of secondary one: fibroplastic renal artery stenosis, atheromatous renal artery stenosis, Conn’s syndrome, renal cystic disease and pheochromocystoma). The decision on the type of hypertension is made only on base on blood pressure, general information and basis biochemical data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and application to boosting. Journal of Computer and System Science 55(1), 119–139 (1997)
Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm. In: Proceedings of the International Conference on Machine Learning, pp. 148–156 (1996)
Jain, A.K., Duin, P.W., Mao, J.: Statistical Pattern Recognition: A Review. IEEE Transaction on Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)
Mitchell, T.: Machine Learning. McGraw Hill, New York (1997)
Opitz, D., Maclin, R.: Popular Ensemble Methods: An Empirical Study. Journal of Artificial Intelligence Research 11, 169–198 (1999)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Quinlan, J.R.: Bagging, Boosting, and C4.5. In: Proceedings of the 13th National Conference on Artificial Intelligence and Eighth Innovative Applications of Artificial Intelligence Conference, AAAI 1996, IAAI 1996, Portland, Oregon, August 4-8, vol. 1, pp. 725–230 (1996)
Schapire, R.E.: The boosting approach to machine learning: An overview. In: Proc. Of MSRI Workshop on Nonlinear Estimation and Classification, Berkeley, CA (2001)
Shapire, R.E.: The Strength of Weak Learnability. Machine Learning 5, 197–227 (1990)
Schapire, R.E.: A Brief Introduction to Boosting. In: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (1999)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Pub., San Francisco (2000)
Blinowska, A., Chatellier, G., Bernier, J., Lavril, M.: Bayesian Statistics as Applied to Hypertension Diagnosis. IEEE Transaction on Biomedical Engineering 38(7), 699–706 (1991)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Data Mining, Inference, and Prediction. Springer Series in Statistics. Springer, New York (2001)
Puchala, E.: A Bayes Algorithm for the Multitask Pattern Recognition Problem – direct and decomposed approach. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds.) ICCSA 2004. LNCS, vol. 3046, pp. 39–45. Springer, Heidelberg (2004)
Koszalka, L., Skworcow, P.: Experimentation system for efficient job performing in veterinary medicine area. In: Gervasi, O., Gavrilova, M.L., Kumar, V., Laganá, A., Lee, H.P., Mun, Y., Taniar, D., Tan, C.J.K. (eds.) ICCSA 2005. LNCS, vol. 3483, pp. 692–701. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wozniak, M. (2005). Boosted Decision Trees for Diagnosis Type of Hypertension. In: Oliveira, J.L., Maojo, V., Martín-Sánchez, F., Pereira, A.S. (eds) Biological and Medical Data Analysis. ISBMDA 2005. Lecture Notes in Computer Science(), vol 3745. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573067_23
Download citation
DOI: https://doi.org/10.1007/11573067_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29674-4
Online ISBN: 978-3-540-31658-9
eBook Packages: Computer ScienceComputer Science (R0)