Mining interesting rules and patterns for salt sensitivity of blood pressure

• Autorzy: Korzeń M. , Klęsk P. , Ciechanowicz A. , Kaczmarczyk M. , Widecka K. , Jaroszewicz S.
• Języki publikacji: EN

Abstrakty

EN

Given a medical data set containing genetic description of sodium-sensitive and nonsensitive patients, we examine it using several techniques: induction of decision rules, naive Bayes classifier, voting perceptron classifier, decision trees, SVM classifier. We specifically focus on induction of decision rules and so called Pareto-optimal rules, which are of large interpretative value for physicians. We find statistically relevant combinations of attributes, which affect the sodium sensitivity.

Słowa kluczowe

EN

data mining; rule induction; genetic data; sodium sensitivity

• Wydawca: Komisja Informatyki Polskiej Akademii Nauk, Oddział w Gdańsku
• Czasopismo: Metody Informatyki Stosowanej
• Rocznik: 2008
• Tom: nr 3 (Tom 16)
• Strony: 149--157
• Opis fizyczny: Bibliogr. 9 poz., rys., tab.

Twórcy

autor

Korzeń M.

• Szczecin University of Technology, Faculty of Computer Science and Information Technology

autor

Klęsk P.

• Szczecin University of Technology, Faculty of Computer Science and Information Technology

autor

Ciechanowicz A.

• Pomeranian Medical University

autor

Kaczmarczyk M.

• Pomeranian Medical University

autor

Widecka K.

• Pomeranian Medical University

autor

Jaroszewicz S.

• National Institute of Telecommunications

Bibliografia

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• [3] Rodriguez-Iturbe B., Vaziri N. D. Salt-sensitive hypertension – update on novel findings. Nephrol Dial Transplant 22, 2007, pp. 992–995.
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• [5] Smyth P., Goodman R. M. An information theoretic approach to rule induction from databases. IEEE Transactions on Knowledge and Data Engineering 4, 1992, pp. 301–316.
• [6] Gray R. M. Entropy and Information Theory. Springer Verlag, New York, USA Information Systems Laboratory, Electrical Engineering Department, Stanford University, 1990.
• [7] Chaitin G. J. Algorithmic Information Theory. Cambridge University Press, Cambridge, 1997.
• [8] Quinlan R. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
• [9] Roberto J., Bayardo J., Agrawal R. Mining the most interesting rules. In: KDD '99: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA, ACM, 1999, pp. 145–154.