Adriana Albu, Radu-Emil Precup, Teodor-Adrian Teban

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The aim of this paper is to present several approaches by which technology can assist medical decision-making. This is an essential, but also a difficult activity, which implies a large number of medical and technical aspects. But, more important, it involves humans: on the one hand, the patient, who has a medical problem and who requires the best solution; on the other hand, the physician, who should be able to provide, in any circumstances, a decision or a prediction regarding the current and the future medical status of the patient. The technology, in general, and particularly the Artificial Intelligence (AI) tools could help both of them, and it is assisted by appropriate theory regarding modeling tools. One of the most powerful mechanisms that can be used in this field is the Artificial Neural Networks (ANNs). This paper presents some of the results obtained by the Process Control group of the Politehnica University Timisoara, Romania, in the field of ANNs applied to modeling, prediction and decision-making related to medical systems. An Iterative Learning Control-based approach to batch training a feedforward ANN architecture is given. The paper includes authors’ concerns in this domain and emphasizes that these intelligent models, even if they are artificial, are able to make decisions, being useful tools for prevention, early detection and personalized healthcare.


Artificial Neural Networks, Medical Diagnosis, Medical Prediction, Prosthetic Hands, Recurrent Neural Networks

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Russell, S.J., Norvig, P., 2010, Artificial Intelligence: A Modern Approach, 3rd ed., Pearson, Upper Saddle River, NJ, USA.

Zurada, J.M., 2012, Introduction to Artificial Neural Systems, Jaico Publishing House, Mumbai, India.

Albu, A., 2009, Decisional methods applied in medical domain, Proc. 5th International Symposium on Applied Computational Intelligence and Informatics, Timisoara, Romania, pp. 123-128.

Albu, A., Stanciu, L., 2015, Benefits of using artificial intelligence in medical predictions, Proc. 5th IEEE International Conference on E-Health and Bioengineering, Iasi, Romania, pp. 1-4.

Teban, T.-A., Precup, R.-E., de Oliveira, T.E.A., Petriu, E.M., 2-016, Recurrent dynamic neural network model for myoelectric-based control of a prosthetic hand, Proc. 2016 IEEE International Systems Conference, Orlando, FL, USA, pp. 1-6.

Teban, T.-A., Precup, R.-E., Lunca, E.-C., Albu, A., Bojan-Dragos, C.-A., Petriu, E.M., 2018, Recurrent neural network models for myo-electric-based control of a prosthetic hand, Proc. 22nd International Conference on System Theory, Control and Computing, Sinaia, Romania, pp. 1-6.

Radac, M.-B., Precup, R.-E., Petriu, E.M., Preitl, S., 2014, Iterative data-driven controller tuning with actuator constraints and reduced sensitivity, Journal of Aerospace Information Systems, 11(9), pp. 551-564.

Radac, M.-B., Precup, R.-E., Petriu, E.M., Preitl, S., 2014, Iterative data-driven tuning of controllers for nonlinear systems with constraints, IEEE Transactions on Industrial Electronics, 61(11), pp. 6360-6368.

Radac, M.-B., Precup, R.-E., Petriu, E.M., 2015, Constrained data-driven model-free ILC-based reference input tuning algorithm, Acta Polytechnica Hungarica, 12(1), pp. 137-160.

Radac, M.-B., Precup, R.-E., 2015, Data-based two-degree-of-freedom iterative control approach to constrained non-linear systems, IET Control Theory & Applications, 9(7), pp. 1000-1010.

Radac, M.-B., Precup, R.-E., 2016, Three-level hierarchical model-free learning approach to trajectory tracking control, Engineering Applications of Artificial Intelligence, 55, pp. 103-118.

Radac, M.-B., Precup, R.-E., Roman, R.-C., 2017, Model-free control performance improvement using virtual reference feedback tuning and reinforcement Q-learning, International Journal of Systems Science, 48(5), pp. 1071-1083.

Radac, M.-B., Precup, R.-E., 2018, Data-driven model-free slip control of anti-lock braking systems using reinforcement Q-learning, Neurocomputing, 275, pp. 317-329.

Radac, M.-B., Precup, R.-E., Roman, R.-C., 2018, Data-driven model reference control of MIMO vertical tank systems with model-free VRFT and Q-learning, ISA Transactions, 73, pp. 227-238.

Albu, A., Precup, R.-E., Teban, T.-A., 2018, Medical applications of artificial neural networks, Proc. XIV International SAUM Conference on Systems, Automatic Control and Measurements, Nis, Serbia, pp. 1-11.

Zare, A., Zare, M.-A., Zarei, N., Yaghoobi, R., Zare, M.-A., Salehi, S., Geramizadeh, B., Malekhosseini, S.-A., Azarpira, N., 2017, A neural network approach to predict acute allograft rejection in liver transplant recipients using routine laboratory data, Hepatitis Monthly, 17(12), paper e55092.

Bertolaccini, L., Solli, P., Pardolesi, A., Pasini, A., 2017, An overview of the use of artificial neural networks in lung cancer research, Journal of Thoracic Disease, 9(4), pp. 924-931.

Korkmaz, S.-A., Binol, H., Akcicek A., Korkmaz, M.-F., 2017, An expert system for stomach cancer images with Artificial Neural Network by using HOG Features and Linear Discriminant Analysis: HOG_LDA_ANN, Proc. IEEE 15th International Symposium on Intelligent Systems and Informatics, Subotica, Serbia, pp. 327-332, 2017.

Esteva, A., Kuprel, B., Novoa, R.-A., Ko, J., Swetter, S.-M., Blau, H.-M., Thrun, S., 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542(7639), pp. 115-118.

Lee, E.-J., Kim, Y.-H., Kim, N., Kang, D.-W., 2017, Deep into the brain: artificial intelligence in stroke imaging, Journal of Stroke, 19(3), pp. 277-285.

Chen, J.X., Xing, Y.W., Xi, G.C., Chen, J., Yi, J.Q., Zhao, D.B., Wang, J., 2007, A comparison of four data mining models: Bayes, neural network, SVM and decision trees in identifying syndromes in coronary heart disease, Proc. 4th International Symposium on Neural Networks, Nanjing, China, pp. 1274-1279.

Udayakumar, E., Santhi, S., Vetrivelan, P., 2017, An investigation of Bayes algorithm and neural networks for identifying the breast cancer, Indian Journal of Medical and Paediatric Oncology, 38(3), pp. 340-344.

Johnson, K.-W., Soto, J.-T., Glicksberg, B.-S., Shameer, K., Miotto, R., Ali, M., Ashley, E., Dudley, J.-T., 2018, Artificial intelligence in cardiology, Journal of the American College of Cardiology, 71(23), pp. 2668-2679.

Filimon, D.-M., Albu, A., 2014, Skin diseases diagnosis using artificial neural networks, Proc. 9th IEEE International Symposium on Applied Computational Intelligence and Informatics, Timisoara, Romania, pp. 189-194.

Albu, A., Pasca, M.-S., Zimbru, C.-G., 2019, Medical predictions: Bayes’ theorem vs artificial neural networks, Proc. 13th IEEE International Symposium on Applied Computational Intelligence and Informatics, Timisoara, Romania, pp. 1-4.

Tanasoiu, I., Albu, A., 2017, A connectionist model for cerebrovascular accident risk prediction, Proc. 6th IEEE International Conference on E-Health and Bioengineering, Sinaia, Romania, pp. 45-48.

UCI Machine Learning Repository - Dermatology Data Set,, last accessed 2018.

World Health Organization,, last accessed 2019.

Avram, R., 2012, Elemente de clinică medicală: aparat cardiovascular, Editura Orizonturi Universitare, Timisoara (in Romanian).

Asadi, H., Dowling, R., Yan, B., Mitchell, P., 2014, Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy, PLoS ONE, 9(2), paper e88225.

Lukić, S., Ćojbasić, Ž., Perić, Z., Milošević, Z., Spasić, M., Pavlović, V., Milojević, A., 2012, Artificial neural networks based early clinical prediction of mortality after spontaneous intracerebral hemorrhage, Acta Neurologica Belgica, 112(4), pp. 375-382.

Precup, R.-E., Teban, T.-A., Petriu, E.M., Albu, A., Mituletu, I.-C., 2018, Structure and evolving fuzzy models for prosthetic hand myoelectric-based control systems, Proc. 26th Mediterranean Conference on Control and Automation, Zadar, Croatia, pp. 625-630.

Hochreiter, S., Schmidhuber, J., 1997, Long short-term memory, Neural Computation, 9(8), pp. 1735-1780.

Gers, F.A., Schmidhuber, J., Cummins, F., 2000, Learning to forget: continual prediction with LSTM, Neural Computation, 12(10), pp. 2451-2471.

Precup, R.-E., Preitl, S., 1997, Popov-type stability analysis method for fuzzy control systems, Proc. Fifth European Congress on Intelligent Technologies and Soft Computing, Aachen, Germany, 2, pp. 1306-1310.

Angelov, P., Victor, J., Dourado, A., Filev, D., 2004, On-line evolution of Takagi-Sugeno fuzzy models, IFAC Proceedings Volumes, 37(16), pp. 67-72.

Precup, R.-E., Preitl, S., Balas, M., Balas, V., 2004, Fuzzy controllers for tire slip control in anti-lock braking systems, Proc. IEEE International Conference on Fuzzy Systems, Budapest, Hungary, 3, pp. 1317-1322.

Precup, R.-E., Tomescu, M.L., Preitl, S., Petriu, E.M., Fodor, J., Pozna, C., 2013, Stability analysis and design of a class of MIMO fuzzy control systems, Journal of Intelligent and Fuzzy Systems, 25(1), pp. 145-155.

Blažič, S., Škrjanc, I., Matko, D., 2014, A robust fuzzy adaptive law for evolving control systems, Evolving Systems, 5(1), pp. 3-10.

Andoga, R., Fozo, L., 2017, Near magnetic field of a small turbojet engine, Acta Physica Polonica A, 131(4), pp. 1117-1119.

Baranyi, P., Tikk, D., Yam, Y., Patton, R.J., 2003, From differential equations to PDC controller design via numerical transformation, Computers in Industry, 51(3), pp. 281-297.

Precup, R.-E., Tomescu, M.L., Preitl, S., 2007, Lorenz system stabilization using fuzzy controllers, International Journal of Computers Communications and Control, 2(3), pp. 279-287.

Navarro, G., Umberger, D.K., Manic, M. 2017, VD-IT2, Virtual Disk cloning on disk arrays using a type-2 fuzzy controller, IEEE Transactions on Fuzzy Systems, 25(6), pp. 1752-1764.

Alvarez Gil, R.P., Johanyák, Z.C., Kovács, T., 2018, Surrogate model based optimization of traffic lights cycles and green period ratios using microscopic simulation and fuzzy rule interpolation, International Journal of Artificial Intelligence, 16(1), pp. 20-40.

Rotariu, C., Manta, V., Costin, H., 2012, Wireless remote monitoring system for patients with cardiac pacemakers, Proc. 2012 IEEE International Conference and Exposition on Electrical and Power Engineering, Iasi, Romania, pp. 845-848.

Haidegger, T., Kovács, L., Precup, R.-E., Benyó, B., Benyó, Z., Preitl, S., Simulation and control for telerobots in space medicine, Acta Astronautica, 181(1), pp. 390-402.

Costin, H., 2013, Fuzzy rules-based segmentation method for medical images analysis, International Journal of Computers Communications and Control, 8(2), pp. 196-205.

Takács, Á., Kovács, L., Rudas, I.J., Precup, R.-E., Haidegger, T., 2015, Models for force control in telesurgical robot systems, Acta Polytechnica Hungarica, 12(8), pp. 95-114.

Belean, B., Streza, M., Crisan, S., Emerich, S., 2017, Dorsal hand vein pattern analysis and neural networks for biometric authentication, Studies in Informatics and Control, 26(3), pp. 305-314.

Kovács, L., 2017, A robust fixed point transformation-based approach for type 1 diabetes control, Nonlinear Dynamics, 89(4), pp. 2481-2493.

Melin, P., Miramontes, I., Prado-Arechiga, G., 2018, A hybrid model based on modular neural networks and fuzzy systems for classification of blood pressure and hypertension risk diagnosis, Expert Systems with Applications, 107, pp. 146-164.

Precup, R.-E., Teban, T.-A., Albu, A., Szedlak-Stinean, A.-I., Bojan-Dragos, C.-A., 2018, Experiments in incremental online identification of fuzzy models of finger dynamics, Romanian Journal of Information Science and Technology, 21(4), pp. 358-376.

Korondi, P., Hashimoto, H., Gajdar, T., Suto, Z., 1996, Optimal sliding mode design for motion control, Proc. 1996 IEEE International Symposium on Industrial Electronics, Warsaw, Poland, pp. 277-282.

Filip, F.G., 2008, Decision support and control for large-scale complex systems, Annual Reviews in Control, 32(1), pp. 61-70.

Antić, D., Nikolić, S., Milojković, M., Danković, N., Jovanović, Z., Perić, S., 2011, Sensitivity analysis of imperfect systems using almost orthogonal filters, Acta Polytechnica Hungarica, 8(6), pp. 79-94.

Osaba, E., Yang, X.-S., Diaz, F., Onieva, E., Masegosa, A., Perallos, A., 2017, A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy, Soft Computing, 21(18), pp. 5295-5308.

Nikolić, V., Milovančević, M., Petković, D., Jocić, D., Savić, M., 2018, Parameters forecasting of laser welding by the artificial intelligence techniques, Facta Universitatis, Series: Mechanical Engineering, 16(2), pp. 193-201.

Dumitrache, I., Constantin, N., Drăgoicea, M., 1996, Retele neurale: identificarea si conducerea proceselor, Matrix Rom, Bucharest (in Romanian).

Alique, A., Haber, R.E., Haber, R.H., Ros, S., Gonzalez, C., 2000, Neural network-based model for the prediction of cutting force in milling process. A progress study on a real case, Proc. 15th IEEE International Symposium on Intelligent Control, Patras, Greece, pp. 121-125.

Azadeh, A., Babazadeh, R., Asadzadeh, S.M., 2013, Optimum estimation and forecasting of renewable energy consumption by artificial neural networks, Renewable and Sustainable Energy Reviews, 27, pp. 605-612.

Tran, T.V., Wan, Y.N., 2017, Artificial chemical reaction optimization algorithm and neural network based adaptive control for robot manipulator, Control Engineering and Applied Informatics, 19(2), pp. 61-70.

Pozna, C., Precup, R.-E., Tar, J.K., Škrjanc, I., Preitl, S., 2010, New results in modelling derived from Bayesian filtering, Knowledge-Based Systems, 23(2), pp. 182-194.

Precup, R.-E., Preitl, S., 2003, Development of fuzzy controllers with non-homogeneous dynamics for integral-type plants, Electrical Engineering, 85(3), pp. 155-168.

Niu, B., Fan, Y., Wang, H., Li, L., Wang, X., 2011, Novel bacterial foraging optimization with time-varying chemotaxis step, International Journal of Artificial Intelligence, 7(A11), pp. 257-273.

Khmelev, A., Kochetov, Y., 2015, A hybrid local search for the split delivery vehicle routing problem, International Journal of Artificial Intelligence, 13(1), pp. 147-164.

Mls, K., Cimler, R., Vaščák, J., Puheim, M., 2017, Interactive evolutionary optimization of fuzzy cognitive maps, Neurocomputing, 232, pp. 58-68.

Li, Y.-Q., Hou, Z.-S., Feng, Y.-J., Chi, R.-H., 2017, Data-driven approximate value iteration with optimality error bound analysis, Automatica, 78, pp. 79-87.

Precup, R.-E., David, R.-C., Szedlak-Stinean, A.-I., Petriu, E.M., Dragan, F., 2017, An easily understandable grey wolf optimizer and its application to fuzzy controller tuning, Algorithms, 10(2), pp. 1-15.



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