Assessment of diagnostic features in the coronary artery disease (CAD) by application of statistical methods and neural networks

• Autorzy: Stanisz-Wallis K. , Izworski A. , Dembinska-Kiec A. , Tadeusiewicz R. , Lech T.
• Konferencja: Computers in Medical Applications: XIIIth Biocybernetics and Biomedical Engineering Conference (10-13 September 2003, Gdańsk, Poland)
• Języki publikacji: EN

Abstrakty

EN

The present work is aimed at comparing the effectiveness of two different methods of risk factor assessment used for prediction of the CAD (coronary artery disease): the logistic regression method and the application of artificial neural networks. The former is widely used in medical research, while the latter is relatively rare. In the logistic regression method hierarchical analysis was employed to select the significant variables of the classification process. In the neural network approach several strategies were proposed for selection of the discriminative variables, all based on weight analysis in the constructed networks. Both methods have produced a consistent set of discriminative variables (Glu0, Ins0, Ins30, BMI, apoA1 and HDL-Ch), belonging to three groups of risk factors associated with insulin resistance, obesity and lipid disorders.

Słowa kluczowe

EN

coronary artery disease; logistic regression method; neural networks

• Wydawca: Politechnika Gdańska
• Czasopismo: TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk
• Rocznik: 2004
• Tom: Vol. 8, No 2
• Strony: 287--295
• Opis fizyczny: Bibliogr. 7 poz., tab.

Twórcy

autor

Stanisz-Wallis K.

• Department of Bioinformatics and Telemedicine, Collegium Medicum Jagiellonian University, Kopernika 17, 31-501 Cracow, Poland

autor

Izworski A.

• Laboratory of Biocybernetics, Dept. of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Cracow, Poland

autor

Dembinska-Kiec A.

• Department of Clinical Biochemistry, Collegium Medicum Jagiellonian University, Kopernika 15, 31-501 Cracow, Poland

autor

Tadeusiewicz R.

• Laboratory of Biocybernetics, Dept. of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Cracow, Poland

autor

Lech T.

• Laboratory of Biocybernetics, Dept. of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Cracow, Poland

Bibliografia

• [1] Stanisz-Wallis K, Izworski A, Lech T and Dembińska-Kieć A 2001 Proc. 12 th Conf. Biocybernetics and Biomedical Engineering, Warsaw, Poland, pp. 800–804 (in Polish)
• [2] Stanisz-Wallis K, Lech T, Izworski A, Kwaśniak M, Dembińska-Kieć A 2000 Proc. 5 th Conf. Polish Neural Network and Soft Computing, Zakopane, Poland, pp. 574–579
• [3] Gordon T, Kannel W B and Castelli W P 1981 Arch. Intern. Med. 141 1128
• [4] Gotto A M 2001 Circulation 1 59
• [5] Assman G 2001 Am. J. Cardiol. 87 (5A) 2B
• [6] Boden W E Am. J. Cardiol. 86 (12A) 19L
• [7] Wierzbicki A S and Mikhailidis D P 2002 Curr. Med. Res. Opin. 18 (1) 36