PRINCIPLES FOR THE DEVELOPMENT OF AN INTELLIGENT HEPATOCELLULAR CARCINOMA STAGING SYSTEM - Problems of Information Technology

PRINCIPLES FOR THE DEVELOPMENT OF AN INTELLIGENT HEPATOCELLULAR CARCINOMA STAGING SYSTEM - Problems of Information Technology

PRINCIPLES FOR THE DEVELOPMENT OF AN INTELLIGENT HEPATOCELLULAR CARCINOMA STAGING SYSTEM - Problems of Information Technology

PRINCIPLES FOR THE DEVELOPMENT OF AN INTELLIGENT HEPATOCELLULAR CARCINOMA STAGING SYSTEM - Problems of Information Technology

PRINCIPLES FOR THE DEVELOPMENT OF AN INTELLIGENT HEPATOCELLULAR CARCINOMA STAGING SYSTEM - Problems of Information Technology
PRINCIPLES FOR THE DEVELOPMENT OF AN INTELLIGENT HEPATOCELLULAR CARCINOMA STAGING SYSTEM - Problems of Information Technology
AZERBAIJAN NATIONAL ACADEMY OF SCIENCES

№1, 2021

PRINCIPLES FOR THE DEVELOPMENT OF AN INTELLIGENT HEPATOCELLULAR CARCINOMA STAGING SYSTEM

Masuma G. Mammadova, Nuru Y. Bayramov, Zarifa G. Jabrayilova, Minara I. Manafli, Mehriban R. Huseynova

The article proposes the principles of developing a support system for the adoption of medical decisions for determining the stages of hepatocellular carcinoma (HCC), which is the most common among malignant liver tumors. HCC is characterized by a set of clinical manifestations of critical conditions, each of which, in turn, is determined by a variety of clinical signs. Specific clinical manifestations of the state of HCC are expressed by various combinations of the values of the signs, which determine the multivariance of possible situations. Establishment of the stages of HCC requires a classification of many possible situations according to given classes and is the basis for choosing a treatment regimen for the disease. In conditions of multivariate situations in order to prevent possible medical errors, the task of developing an intelligent system for determining the stages of HCC is set. In accordance with the methodological approach to the development of intelligent systems, a conceptual model of the problem of determining the stages of HCC is proposed, the basic concepts and their relationship are determined. To transform the obtained expert knowledge into a system, a production model of knowledge representation is used, rules that form the knowledge base are developed. The structure of the computer system for determining the stages of HCC is developed, the principles of operation of its constituent blocks are shown. The working windows of the system implemented on the Delphi software platform are presented, the decision-making mechanism is described. An intelligent system developed to determine the stages of HCC can be used as a computer assistant for a doctor in the process of diagnostic and treatment decisions (pp.3-14).

Keywords: the stages of hepatocellular carcinoma, intelligent system, knowledge base, production model, rules, decision making.
DOI : 10.25045/jpit.v12.i1.01
References
  • Məmmədova M.H., Cəbrayılova Z.Q. Tibbi ekspert sistemlərin yaradılması problemləri və inkişaf istiqamətləri // İnformasiya texnologiyaları problemləri, 2017, №1, s.81–91.
  • Məmmədova M.H., Cəbrayılova Z.Q. Elektron-tibb: formalaşması və elmi-nəzəri problemləri, Bakı: “İnformasiya Texnologiyaları” nəşriyyatı, 2019, 350 s.
  • Колоденкова А.Е., Новокщенов C.Г. Интеллектуальная система поддержки принятия решений для диагностики и выбора схем лечения пациента / XIII Всероссийское совещание по проблемам управления-ВСПУ-2019, Москва 17-20 июня 2019 г., c.1879–1883.
  • Bayramov N.Y. Qaraciyərin cərrahi xəstəlikləri. Bakı-2012, ISBN 978-9952-460-27-8, 325
  • Manchini M. Exploiting Big Data for improving healthcare services// Journal of e-Learning and Knowledge Society, 2014, vol.10, no.2, pp.23–33.
  • Big Data in Human Resource Management – Developing Research Context. www.researchgate.net/publication/275520745
  • Знаменская Т. Зачем нужны ИТ в здравоохранении? // Открытые системы, 2010, №02. www.osp.ru/os/2010/02/13001446/
  • Miller G.A. The magical number seven, plus or minus two: Some limits on our capacity for processing information // Psychological Review, 1956, vol.63, pp.81–97.
  • Абдуллаева Г.Г., Мамедова М.В. Экспертная система распознавания функционального состояния щитовидной железы в случаях трудной диагностики // Известия НАНА, Серия физико-технических и математических наук, 2003, №2–3, с.126–129.
  • Abdullayeva Q.Ə. Sud vəzi şişlərinin informasiya-diaqnostik sisteminin işlənməsi: tex.elm.nam.dis. avtoref. , Bakı, 2004, 20 s.
  • Hacıyev Z.Ə. Ortopediyada cərrahi mudaxilə seciminin intellektual sistemi: tex.elm.nam....dis. avtoref., Bakı, 2005, 20 s.
  • Şükürlü S.F. Oftalmologiya sahəsi üzrə ambulatoq xəstələrin ilkin diaqnostikası üçün ekspert sistemi: tex.elm.nam.dis. avtoref., Bakı, 2005, 20 s.
  • Məmmədova M., Amooji A. Epilepsiya xəstəliyinin diaqnostikası üzrə ekspert sistemi / “Elektron tibbin multidissiplinar problemləri” I Respublika elmi-praktiki konfransının əsərləri, Bakı, 25 may, 2015, s.211–214.
  • Вагин В.Н., Еремеев А.П. Некоторые базовые принципы построения интеллектуальных систем поддержки принятия решений реального времени // Изв. РАН. ТиСУ, 2001, № 6, с.114–123.
  • Зо М.Т. Методы и программные средства ускорения поиска решения в базах знаний нечётких экспертных систем. Авт. дисс. на соискание ученой степени кандидата технических наук, Москва, 2018, 154 с.
  • Xiaopu S., Fenfang W., Di W., Shan L., Jingyi L., Nan Z., Xiaoni C., Anlong X. Human Hepatic Cancer Stem Cells (HCSCs) Markers Correlated With Immune Infiltrates Reveal Prognostic Significance of Hepatocellular Carcinoma // Frontiers in Genetics., 28 February 2020. https://doi.org/10.3389/fgene.2020.00112
  • Gbolahan O.B., Schacht M.A., Beckley E.W., LaRoche T.P., O'Neil B.H., Pyko M. Locoregional and systemic therapy for hepatocellular carcinoma // Journal of Gastrointestinal Oncology, 2017, vol.8, no.2, pp.215–228.
  • Aman S., Babita P. An Efficient Diagnosis System for Detection of Liver Disease Using a Novel Integrated Method Based on Principal Component Analysis and K-Nearest Neighbor (PCA-KNN) // International Journal of Healthcare Information Systems and Informatics, 2016, vol.11, no.4, pp.56–
  • Ozyilmaz L., Yildirim T. Artificial neural networks for diagnosis of hepatitis disease // Int. Jt.Conf. Neural Networks, 2003. doi:10.1109/IJCNN.2003.1223422
  • Sartakhti J.S., Zangooei M.H., Mozafari K. Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA) // Computer Methods and Programs in Biomedicine, 2015, vol.108, no.2, pp.570–579.
  • Revett K., Gorunescu F., Gorunescu M., Ene M. Mining A Primary Biliary Cirrhosis Dataset Using Rough Sets and a Probabilistic Neural Network / Proceedings of the 2006 3rd International IEEE Conference Intelligent Systems, 2006, pp.284–289.
  • Obot O.U., Udoh S.S. A framework for fuzzy diagnosis of hepatitis // Proceedings of the 2011 World Congr. Inf. Commun. Technol., 2011, pp.439–443.
  • Li B. N., Chui C. K., Chang S., Ong S. H. A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images // Expert Systems with Applications, 2012, vol.39, no.10, pp.9661–9668.
  • Ming L. K., Kiong L. C., Soong L. W. Autonomous and deterministic supervised fuzzy clustering with data imputation capabilities // Applied Soft Computing, 2011, vol.11, no.1, pp.1117–1125.
  • Floares A. G. Intelligent clinical decision supports for interferon treatment in chronic hepatitis C and B based on i-biopsy / Proceedings of the 2009 International Joint Conference on Neural Networks, 2009, pp.855–860.
  • Yan W., Lizhuang M., Xiaowei L., Ping L. Correlation between Child-Pugh Degree and the Four Examinations of Traditional Chinese Medicine (TCM) with Liver Cirrhosis / Proceedings of the 2008 Int. Conf. Biomed. Eng. Informatics, 2008, pp.858–862.
  • Kulluk S., Ozbakır L., Baykasoğlu A. Fuzzy DIFACONN-miner: A novel approach for fuzzy rule extraction from neural networks // Expert Systems with Applications, 2013, vol. 40, no.3, pp.938–946.
  • Li D.-C., Liu C.-W. A class possibility based kernel to increase classification accuracy for small data sets using support vector machines // Expert Systems with Applications, 2010, vol.37, no.4, pp.3104–3110.
  • Neshat M., Zadeh A.E. Hopfield neural network and fuzzy Hopfield neural network for diagnosis of liver disorders / Proceedings of the 2010 5th IEEE International Conference Intelligent Systems, 2010, pp.162–167.
  • Li D.-C., Liu C.-W., Hu S.C. A learning method for the class imbalance problem with medical data sets // Computers in Biology and Medicine, 2010, vol.40, no.5, pp.509–518.
  • Mezyk E., Unold O. Mining fuzzy rules using an Artificial Immune System with fuzzy partition learning // Applied Soft Computing, 2011, vol.11, no.2, pp.1965–1974.
  • Polat K., Sahan S., Kodaz H., Güneş Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism // Expert Systems with Applications, 2007, vol.32, no.1, pp.172–183.
  • Dehuri S., Cho S.B. Evolutionarily optimized features in functional link neural network for classification // Expert Systems with Applications, 2010, vol.37, no.6, pp.4379–4391.
  • Gorunescu F., Belciug S., Gorunescu M., Badea R. Intelligent decision-making for liver fibrosis stadialization based on tandem feature selection and evolutionary-driven neural network // Expert Systems with Applications, 2012, vol.39, no.17, pp.12824–12832.
  • Torun Y., Tohumoglu G. Designing simulated annealing and subtractive clustering based fuzzy classifier // Applied Soft Computing, 2011, vol.11, no.2, pp.2193–2201.
  • Aldape-Perez M., Yanez-Marquez C., Camacho-Nieto O. J., Arguelles-Cruz A. An associative memory approach to medical decision support systems // Computer Methods and Programs in Biomedicine, 2012, vol.106, pp.287–307. doi:10.1016/j.cmpb.2011.05.002 PMID:21703713
  • Mirpouya M. Developing an expert system for diagnosing liver diseases // EJERS, European Journal of Engineering Research and Science, 2019, vol.4, no.3. doi.org/10.24018/ejers.2019.4.3.1168.
  • Найданов Ч.А. Cистема поддержки принятия решений для предупреждения рисков возникновения критических состояний // Альманах современной науки и образования, № 8(98) 2015, c.92–95