Preview

Philosophical Problems of IT & Cyberspace (PhilIT&C)

Advanced search

NEURAL NETWORK SYSTEM IN THE BUILDING INFORMATIONMODELS OF DEGREE OF CHANGES OF VASCULAR WALLIN PATIENTS WITH CAROTID ATHEROSCLEROSIS

Abstract

In this article include the system of classification of degree of thickening complex "intima-media" of the common carotid artery (CCA IMT). The main tool for building information models were algorithms and methods for synthesis of artificial neural networks (ANN). The analysis is based on the results of the survey 242 subjaects aged 40-90 years. The mathematical classification for optimizing assessment of carotid atherosclerosis was used. ANN was made up of neurons in the input, hidden and output layers. First, the correct classification of these data was obtained by ultrasound specialists with duplex scanning. ANN model does successfully classified in 84.5%. We found that ANN is effective, when designated four classes. The result of this classification system is rapid establishment of medical diagnosis

About the Authors

G. A. Rozikhodjaeva
Central Clinical Hospital № 1 of Medico-Sanitary Association
Russian Federation


Z. T. Ikramova
Institute of doctor's improvement
Russian Federation


D. A. Rozikhodzjaeva
University of Information technologies
Russian Federation


References

1. Игнатьев Н.А. Извлечение явных знаний из разнотипных данных с помощью нейронных сетей // Вычислительные технологии. — Новосибирск, 2003.- Т.8, №2.- С.69-73.

2. Игнатьев Н.А., Мадрахимов Ш.Ф. О некоторых способах повышения прозрачности нейронных сетей// Вычислительные технологии. — Новосибирск, 2003.- Т.8, №6.- С.31-37.

3. Розыходжаева Г.А. Функциональное состояние сердечнососудистой системы у больных ИБС пожилого и старческого возраста. Дисс.. на соиск. дмн Ташкент 2007.

4. Кунцевич Г.И., Тер-Хачатурова И.Е. Ультразвуковые методы исследования магистральных артерий шеи и артерий виллизиева круга// Методы исследования в неврологии и нейрохирургии/ Под ред. Гусева Е.И., 2000.-С. 146-201.

5. Mobley B.A., Schecter E., Moore W.E., McKee P.A., Eichner J.E. Predictions of coronary artery stenosis by artificial neural network, Artif. Intell.Med. 18 (2000) 187-203.

6. Baxt W.G. Application of neural networks to clinical medicine, Lancet 346 (1995)1135-1138.

7. White H. Learning in artitificial neural networks: a statistical approach, Neural Comput. 1 (1989)425-464.

8. Pignoli P., Tremoli E., Poll A. et al. Intimal plus medial thickness of the arterial wall: a direct measurement with ultrasound imaging. //Circulation. 1986; 74:1399-1406.

9. ECST (European Carotid Surgery Trialists' Collaborative Group), MRC European carotid surgery trial: interim results for symptomatic patients with severe (70-99%) or with mild (0-29%) carotid stenosis, Lancet 337 (1991) 1235-1243.

10. Persson J., Formgren J., Israelsson В., Berglund G. Ultrasound-determined intima-media thickness and atherosclerosis. Direct and indirect validation. //Arterioscler Thromb Vase Biol 1994;14:261-264. //.

11. Bots M.L., Hofinan A, Grobbee D.E. Common carotid intima-media thickness and lower extremity arterial atherosclerosis: the Rotterdam Study. // Arterioscler Thromb. 1994; 14:1885-1891.

12. Adams M.R., NakagomiA., Keech A. et al. Carotid intima-media thickness is only weakly correlated with the extent and severity of coronary artery disease. //Circulation/1995; 92:2127-2134.

13. Zureik M., Ducimetiere P., Touboul P.J. et al. Common carotid intimamedia thickness predicts occurrence of carotid atherosclerotic plaques.// Arterioscler Thromb Vase Biol 2000; 20:1622-1630

14. Serhatl oglu S., Hardalac F., Guler I. Classification of transcranial Doppler signals using artificial neural network, J. Med. Systems 27 (2) (2003) 205-214.


Review

For citations:


Rozikhodjaeva G.A., Ikramova Z.T., Rozikhodzjaeva D.A. NEURAL NETWORK SYSTEM IN THE BUILDING INFORMATIONMODELS OF DEGREE OF CHANGES OF VASCULAR WALLIN PATIENTS WITH CAROTID ATHEROSCLEROSIS. Philosophical Problems of IT & Cyberspace (PhilIT&C). 2012;(2):73-80. (In Russ.)

Views: 110


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2305-3763 (Online)