DETECTING THE UROLOGIC DISEASES BY MEANS OF MULTIFACTORIAL HIERARCHIC NEURAL NETWORKS

Н. И. Федоренко, И. М. Антонян, Р. В. Стецишин, В. С. Харченко

Abstract


The article offers a multifactorial hierarchy structure of neural network modules, developed to diagnose the urologic diseases by processing versatile heterogeneous parameters. These parameters are obtained by carrying out uroflowmetry in patients, using a uroflometer. Based on the results of the analysis, the key parameters for detecting aberrations, obstructions, and diagnosing diseases have been selected. The key feature of the developed multifactorial hierarchy model is the modularized system for detecting the heterogeneous and versatile uroflowmetric parameters. This system is based on neural network modules of different architecture, and training methods. The ability of the neural network model to detect the uroflowmetric parameters in patients has been tested.

Keywords


multifactorial hierarchy structure of neural network modules, detection of the uroflowmetric parameters by means of neural network, neural networks, hierarchic interconnection of the uroflowmetric parameters in patients

References


Kolekar, Jayashri S., Pawar, Chhaya. Clinical decision making using artificial neural network with particle swarm optimization algorithm. International Journal of Research in Advent Technology, January 2014, vol. 2, iss. 1, pp. 311–315.

Al-Shayea, Q. K. Artificial Neural Networks in Medical Diagnosis. Journal of Computer Science, March 2011, vol. 8, iss. 2, pp. 150–155.

Arzamassev, A. A., Zenkova, N. A., Neudakhin, A. V. Tekhnologiya postroeniya meditsinskoi ekspertnoi sistemy na osnove apparata iskusstvennykh neironnykh setei [The technology of construction of medical expert system based on artificial neural networks]. Informatsionnye tekhnologii, 2009, vol. 8, pp. 60–63.

Abdulaeva, G. G., Kurbanova, N. G.,Mirzazade, I. Kh. Intellektual'no-informatsionnaya sistema differentsial'noi diagnostiki otravlenii toksicheskimi veshchestvami (na primere otravlenii ugarnym gazom) [Intellectually and information system of differential diagnosis of poisoning by toxic substances (for example, carbon monoxide poisoning)]. Informatsionnye tekhnologii, 2013, vol. 10, pp. 40–45.

Ghwanmeh, S., Mohammad, A., Al-Ibrahim, A. Innovative Artificial Neural Networks-Based Decision Support System for Heart Diseases Diagnosis. Journal of Intelligent Learning Systems and Applications, 2013, vol. 5, pp. 176–183.

Irfan Khan, Y., Zope, P. H., Suralkar, S. R. Importance of Artificial Neural Network in Medical Diagnosis disease like acute nephritis disease and heart disease. International Journal of Engineering Science and Innovative Technology, March 2013, vol. 2, iss. 2, pp. 210–217.

Johnsson, M., Garcia Chamizo, J. M., Soriano Paya, A., Ruiz Fernandez, D. Application of artificial neural networks in the diagnosis of urological dysfunctions. Expert Systems with Applications, April 2009, vol. 36, iss. 3, part 2, pp. 5754–5760.

Paya, A., Fernandez, D., Mendez, D., Montejo Hernandez, C. Development of an artificial neural network for helping to diagnose diseases in urology. BIONETICS '06 : Proceedings of the 1st international conference on Bio inspired models of network, information and computing systems Article, 2006, vol. 9, pp. 1–4

Altunay, S., Telatar, Z., Erogul, O., Aydur, E. A new approach to urinary system dynamics problems: Evaluation and classification of uroflowmeter signals using artificial neural networks. Expert Systems With Applications, 2009, vol. 36, iss. 3, pp. 4891–4895.

Altunay, S., Telatar, Z., Erogul, O., Aydur, E. Interpretation of Uroflow Graphs with Artificial Neural Networks. Signal Processing and Communications Application, IEEE 14th, 17–19 April 2006, pp. 1–4.

Lopatkin, N. A. Urologiya: natsional'noe rukovodstvo [Urology: national leadership]. Moscow, Izdatel'skaya gruppa GEOTAR – Media Publ., 2009. 1024 p.

Pushkar', D. Yu., Ka'syan, G. R. Funktsional'naya urologiya i urodinamika [Functional urology and urodynamics]. Moscow, GEOTAR– Media Publ., 2014. 376 p.




DOI: https://doi.org/10.32620/reks.2016.1.01

Refbacks

  • There are currently no refbacks.