Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Khatatneh, Khalafa | Filist, Sergeyb | Al-Kasasbeh, Riad Tahac; * | Aikeyeva, Altyn Amanzholovnad | Namazov, Manafaddine | Shatalova, Olgaf | Shaqadan, Ashrafg | Miroshnikov, Andreyh
Affiliations: [a] Department of Computer, Balqa Applied University, Prince Abdullah bin Ghazi faculty for Communication and Information Technology | [b] Department of Biomedical Engineering, Southwest State University, Kursk | [c] Electrical Energy Department, Balqa Applied University | [d] L.N. Gumilyov Eurasian National University (ENU), Kazakhstan | [e] Baku Engineering University, Khirdalan City | [f] Department of Biomedical Engineering, Southwest State University, Kursk | [g] Civil Engineering Department, Zarqa University | [h] South-West State University, Kursk
Correspondence: [*] Corresponding author. Riad Taha Al-Kasasbeh, Electrical Energy Department, Al-Balqa Applied University, King Hussien, Amman 11937, Jordan. E-mail: [email protected].
Abstract: Modern medical risk classification systems focus on traditional risk factors and modeling methods. The available modeling tools do not allow reliable prediction of the of disease severity. In this study we develop prediction model of recurrent myocardial infarction in the rehabilitation period using several health variables generated in virtual flows. Hybrid decision modules with health data flows were used to build prognostic model for the prediction of disease. The vector of input information features consists of two subvectors: the first reflects real flows, the second reflects virtual flows. Complex interrelations among input data are modelled using Neural Network structure. The model classification quality of the intellectual cardiovascular catastrophe prediction system was tested on a sample composed of 230 patients who had acute myocardial infarction. For prediction, three categories of risk factors were identified: traditional factors, factors associated with stressful overloads, and risk factors derived from bio-impedance studies. During the rehabilitation period, the level of molecular products of lipid peroxidation and the antioxidant potential of blood serum were also studied. Experimental studies of various modifications of the proposed classifier model were conducted, consisting of sequential disconnection from the aggregator of solutions of “weak” classifiers at various hierarchical levels. The mathematical model show predictions accuracy of correct prognosis for the risk of myocardial infarction exceeding 0.86. Prediction quality indicators are higher than the known ASCORE cardiovascular catastrophe prediction system, on average, by 14%.
Keywords: Hybrid decision module, latent variable, GMDH model, neural network, aggregators of fuzzy decision rules, recurrent myocardial infarction
DOI: 10.3233/JIFS-212617
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1621-1632, 2022
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]