New possibilities of artificial intelligence method applications for to simulate the occurrence and development of diseases and optimize their prevention and treatment


Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

This article is devoted to the methodological issues of the application of artificial intelligence techniques in preventive medicine. We showed a specific example of the neural network application allows not only to diagnose cardiovascular diseases, but also on a quantitative basis to predict their emergence and development in future periods of life. This allows you to select the optimal strategy for the prevention and treatment of patients based on their individual parameters. The article concluded: recommendations for the prevention and treatment of cardiac patients should be given strictly individually, taking into account physiological peculiarities of the organism of patients. If for some patients it is useful to give up Smoking, limit the consumption of sweets, take drugs, reduce blood pressure, etc., for other patients, these recommendations may cause harm. Our intelligent system helps to identify such non-standard patients and to avoid incorrect recommendations. The prototype of the proposed system laid out in the «Projects» section on the website www.PermAi.ru.

Full Text

Restricted Access

About the authors

Leonid N. Yasnitsky

Perm State University

Email: yasn@psu.ru
Full Professor Department of applied mathematics and Informatics

Andrey A. Dumler

E.A. Vagner Perm state medical university

Email: rector@psma.ru
PhD of medical Sciences. Associate Professor. The Department of propedeutics of internal diseases No 1

Fyodor M. Cherepanov

Perm State Humanitarian Pedagogical University

Email: fe-c@pspu.ru
Senior lecturer. Department of applied Informatics

References

  1. Amato F., Lopez F., Pena-Méndez E.M., Vanhara P., Hampl A., Havel J. Artificial neural networks in medical diagnosis. J. Appl. Biomed. 2013;11:47-58.
  2. Sandhu I.K., Nair M., Shukla H., Sandhu S.S. Artificial neural network: as emerging diagnostic tool for breast cancer. Int. J. Pharm. Biol. Sci. 2015;5(3):29-41.
  3. Narang S., Verma H.K., Sachdev U. A Review of Breast Cancer Detection using ART Model of Neural Networks. Int. J. Adv. Res. Computer Sci. Software Engineering. 2012;2(10):311-9.
  4. Awwalu J., Garba A.G, Ghazvini A., Atuah R. Artificial Intelligence in Personalized Medicine Application of AI Algorithms in Solving Personalized Medicine Problems. Int. J. Adv. Res. Computer Sci. Software Engineering. 2015;7(6):439-43.
  5. Мустафаев А.Г. Применение искусственных нейронных сетей для ранней диагностики заболевания сахарным диабетом. Кибернетика и программирование. 2016;2:1-7.
  6. Soltani Z., Jafarian A. A New Artificial Neural Networks Approach for Diagnosing Diabetes Disease Type II. Int. J. Adv. Computer Sci. Appl. 2016;7(6):89-95.
  7. Беребин M.A., Пашков C.B. Опыт применения искусственных нейронных сетей для целей дифференциальной диагностики и прогноза нарушений психической адаптации. Вестник Южно-Уральского государственного университета. 2006;14:41- 50
  8. Kuo R.J., Huang M.H., Cheng W.C., Lin C.C., Wu Y.H. Application of a two-stage fuzzy neural network to a prostate cancer prognosis system. Artif. Intell. Med. 2015;63(2):119-33.
  9. Sanoob M.U., Madhu A., Ajesh K.R., Varghese S.M. Artificial neural network for diagnosis of pancreatic cancer. Int. J. Cybernet. Inform. 2016;5(2):40-9.
  10. Ganesan N., Venkatesh K., Rama M.A., Malathi Palani A. Application of Neural Networks in Diagnosing Cancer Disease Using Demographic Data. Int. J. Computer Appl. 2010;1(26):75-85.
  11. Afshar S., Abdolrahmani F., Tanha F.V., Seif M.Z., Taheri K. Recognition and prediction of leukemia with Artificial Neural Network. Med. J. Islam. Rep. Iran. 2011;25(1):35-9.
  12. Mahesh C., Suresh V.G., Babu M. Diagnosing Hepatitis B Using Artificial Neural Network Based Expert System. Int. J. Engineering Innovat. Technol. 2013;3(6):139-44.
  13. Pearce G., Wong J., Mirtskhulava L., Al-Majeed S., Bakuria K., Gulua N. Artificial Neural Network and Mobile Applications in Medical diagnosis. 17th UKSIM-AMSS International Conference on Modelling and Simulation. 2015. рp. 60-5.
  14. Kadhim Q. Artificial Neural Networks in Medical Diagnosis. Int. J. Computer Sci. 2011;8(2):150-5.
  15. Kumar K., Abhishek. Artificial Neural Networks for Diagnosis of Kidney Stones Disease. Int. J. Inform. Technol. Computer Sci. 2012;7:20-5.
  16. Gil D., Johnsson M. Diagnosing Parkinson by using artificial neural networks and support vector machines. Glob. J. Computer Sci. Technol. 2009;9(4):63-71.
  17. Singh M., Singh M., Singh P. Artificial Neural Network based classification of Neuro-Degenerative diseases using Gait features. Int. J. Inform. Technol. Knowl. Manag. 2013;7(1):27-30.
  18. Аравин О.И. Применение искусственных нейронных сетей для анализа патологий в кровеносных сосудах. Российский журнал биомеханики. 2011;15(3):45-51.
  19. Sayad A.T., Halkarnikar P.P. Diagnosis of heart disease using neural network approach. Int. J. Adv. Sci. Engineer. Technol. 2014;2(3):88-92.
  20. Ajam N. Heart Diseases Diagnoses using Artificial Neural Network. Network Complex Syst. 2015;5(4):7-11.
  21. Olaniyi E.O., Oyedotun O.K. Heart Diseases Diagnosis Using Neural Networks Arbitration. Int. J. Intell. Syst. Appl. 2015;12:75-82.
  22. Kojuri J., Boostani R., Dehghani P., Nowroozipour F., Saki N. Prediction of acute myocardial infarction with artificial neural networks in patients with nondiagnostic electrocardiogram. J. Cardiovasc. Dis. Res. 2015;6(2):51-60.
  23. Хливненко Л.А., Васильев В.В., Пятакович Ф.А. Возможности решения медицинских диагностических задач с помощью проектирования обучающихся искусственных нейронных сетей. Успехи современного естествознания. 2010;12:75-9; URL: https://www. natural-sciences.ru/ru/article/view?id=15450 (дата обращения: 04.06.2017).
  24. Shaikh A.B., Sarim M., Raffat S.K., Ahsan K, Nadeem A. and Siddiq M. Artificial Neural Network: A Tool for Diagnosing Osteoporosis. Research. J. Recent Sci. 2014;3(2):87-91.
  25. Raji C.G., Vinod Chandra S.S. Graft survival prediction in liver transplantation using artificial neural network models. J. Computat. Sci. 2016;16:72-8.
  26. Прохоренко И.О. Метод нейросетевого моделирования и его использование для прогнозирования развития соматической патологии у лиц старших возрастных групп // Современные проблемы науки и образования. 2013;1. URL: https://www.science-education.ru/ru/article/view?id=8411 (дата обращения: 11.07.2017).
  27. Ясницкий Л.Н., Думлер А.А., Богданов К.В., Полещук А.Н., Черепанов Ф.М., Макурина Т.В., Чугайнов С.В. Диагностика и прогнозирование течения заболеваний сердечно-сосудистой системы на основе нейронных сетей. Медицинская техника. 2013;3:42-4.
  28. Yasnitsky L.N., Dumler A.A., Poleshchuk A.N., Bogdanov C.V., Cherepanov F.M. Artificial Neural Networks for Obtaining New Medical Knowledge: Diagnostics and Prediction of Cardiovascular Disease Progression. Biol. Med. (Aligarh). 2015;7(2):BM-095-15, 8 pages. (http://www.biolmedonline.com/Articles/Vol7_2_2015/BM-095-15_Artificial-Neural-Networks-for-Obtaining-New-Medical-Knowledge-Diagnostics-and-Prediction-of-Cardiovascular-Disease-Progr.pdf)
  29. Ясницкий Л.Н., Бондарь В.В., Черепанов Ф.М. Пермская научная школа искусственного интеллекта и ее инновационные проекты. 2-е изд. М.; Ижевск: НИЦ «Регулярная и хаотическая динамика», 2008. 75 с.
  30. Ясницкий Л.Н. Интеллектуальные системы: учебник. М.: Лаборатория знаний, 2016. 221 с.
  31. Александров В.В., Стенчиков Г.Л. Об одном вычислительном эксперименте, моделирующем последствия ядерной войны. Вычислительная математика и математическая физика. 1984;24(1):140-4.
  32. Черепанов Ф.М., Ясницкий Л.Н. Нейросимулятор 5.0. Свидетельство о государственной регистрации программы для ЭВМ № 2014618208. Заявка Роспатент № 2014614649. Зарегистрировано в Реестре программ для ЭВМ 12 августа 2014г.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies