Intelligent Disease Diagnosis with Vortex Optimization Algorithm Based ANFIS

Main Article Content

Tuncay Yigit Sena Celik


Artificial intelligence is a very important field since it can provide good solutions for different problems from real world. We can see different solutions of intelligent systems and these solutions generally support people to experience better standars in their life. The field of medical is one of important fields in which artificial intelligence is often used. Objective of this study is to introduce an intelligent diagnosis system using both adaptive neuro fuzzy inferencing system (ANFIS) and vortext optimization algorithm (VOA) to form a diagnosis system for different diseases. In the system, ANFIS is trained by VOA for better training against target disease. The study provides information for the background, developed ANFIS-VOA system and reports some findings for example diseases.

Article Details

How to Cite
YIGIT, Tuncay; CELIK, Sena. Intelligent Disease Diagnosis with Vortex Optimization Algorithm Based ANFIS. Journal of Multidisciplinary Developments, [S.l.], v. 3, n. 1, jan. 2019. ISSN 2564-6095. Available at: <>. Date accessed: 16 feb. 2019.
Natural Sciences - Regular Research Paper


Abbass, H. A. (2002). An evolutionary articial neural networks approach for breast cancer diagnosis. Articial intelligence in Medicine, 25 , 265-281.
Al-Shayea, Q. K. (2011). Articial neural networks in medical diagnosis. International Journal of Computer Science Issues, 8 , 150-154.
Alpaydin, E. (2009). Introduction to machine learning. MIT press.
Amato, F., Lopez, A., Pena-Mendez, E. M., Vanhara, P., Hampl, A., & Havel, J. (2013). Articial neural networks in medical diagnosis.
Avci, E., & Turkoglu, I. (2009). An intelligent diagnosis system based on principle component analysis and ans for the heart valve diseases. Expert Systems with Applications, 36 , 2873{2878.
Azar, A. T., & El-Metwally, S. M. (2013). Decision tree classiers for automated medical diagnosis. Neural Computing and Applications, 23 , 2387-2403.
Blake, C., & Merz, C. (2015). Uci repository of machine learning databases, department of information and computer science, university of california, irvine, ca, 1998. URL: http://www. archive. ics. uci. edu/ml.
Bonabeau, E., Marco, D. d. R. D. F., Dorigo, M., Theraulaz, G., Theraulaz, G. et al. (1999). Swarm intelligence: from natural to articial systems. 1. Oxford university press.
Bonnet, N. (2000). Articial intelligence and pattern recognition techniques in microscope image processing and analysis. In Advances in Imaging and Electron Physics (pp. 1{77). Elsevier volume 114.
Burke, H. B., Goodman, P. H., Rosen, D. B., Henson, D. E., Weinstein, J. N., Harrell Jr, F. E., Marks, J. R., Winchester, D. P., & Bostwick, D. G. (1997).
Articial neural networks improve the accuracy of cancer survival prediction. Cancer, 79 , 857-862.
Chen, C.-M., Chou, Y.-H., Han, K.-C., Hung, G.-S., Tiu, C.-M., Chiou, H.-J., & Chiou, S.-Y. (2003). Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and articial neural networks. Radiology, 226 , 504-514.
Demir, A., & Kose, U. (2017). Solving optimization problems via vortex optimization algorithm and cognitive development optimization algorithm. BRAIN. Broad Research in Articial Intelligence and Neuroscience, 7 , 23-42.
Elmas, C. (2007). Articial intelligence applications (In Turkish). Seckin Press.
Fan, C.-Y., Chang, P.-C., Lin, J.-J., & Hsieh, J. (2011). A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classication. Applied Soft Computing, 11 , 632-644.
Ghasemi, E., Kalhori, H., & Bagherpour, R. (2016). A new hybrid ans-pso model for prediction of peak particle velocity due to bench blasting. Engineering with Computers, 32 , 607-614.
Ginsberg, M. (2012). Essentials of articial intelligence. Newnes. 14
Guo, L., Rivero, D., Seoane, J. A., & Pazos, A. (2009). Classication of eeg signals using relative wavelet energy and articial neural networks. In Proceedings of the rst ACM/SIGEVO Summit on Genetic and Evolutionary Computation (pp. 177-184). ACM.
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classication using support vector machines. Machine learning, 46, 389-422.
Hemanth, J. D., Kose, U., Deperlioglu, O., & de Albuquerque, V. H. C. (2018). An augmented reality-supported mobile application for diagnosis of heart diseases. The Journal of Supercomputing, (pp. 1-26).
Holland, J. H. (1992). Genetic algorithms. Scientic american, 267 , 66-73.
Hosseini, M. S., & Zekri, M. (2012). Review of medical image classication using the adaptive neuro-fuzzy inference system. Journal of medical signals and sensors, 2, 49.
Hu, Y. H., Tompkins, W. J., Urrusti, J. L., & Afonso, V. X. (1993). Applications of articial neural networks for ecg signal detection and classication. Journal of electrocardiology, 26 , 66-73.
Ibrahim, S., Khalid, N. E. A., & Manaf, M. (2010). Seed-based region growing (sbrg) vs adaptive network-based inference system (ans) vs fuzzy c-means (fcm): brain abnormalities segmentation. International Journal of Electrical and Computer Engineering, 5 , 94-104.
Jang, J.-S. (1993). Ans: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23 , 665-685.
Jang, J.-S. R., Sun, C.-T., & Mizutani, E. (1997). Neuro-fuzzy and soft computing; a computational approach to learning and machine intelligence, .
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Articial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2 , 230-243.
Kalaiselvi, C., & Nasira, G. (2014). A new approach for diagnosis of diabetes and prediction of cancer using ans. In Computing and Communication Technologies (WCCCT), 2014 World Congress on (pp. 188-190). IEEE.
Karakoc, M. M. (2018). Prediction of electroencephalogram time series via articial neuro-fuzzy inference system trained by league championship algorithm. In Nature-Inspired Intelligent Techniques for Solving Biomedical Engineering Problems (pp. 232-248). IGI Global.
Kennedy, J. (2011). Particle swarm optimization. In Encyclopedia of machine learning (pp. 760-766). Springer.
Khan, J., Wei, J. S., Ringner, M., Saal, L. H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C. R., Peterson, C. et al. (2001). Classification and diagnostic prediction of cancers using gene expression proling and articial neural networks. Nature medicine, 7 , 673.
Kononenko, I. (2001). Machine learning for medical diagnosis: history, state of the art and perspective. Articial Intelligence in medicine, 23 , 89-109.
Kose, U. (2017). Development of Articial intelligence based optimization algorithms. Ph.D. thesis Selcuk University Institute of Naturel Sciences.
Kose, U., & Arslan, A. (2015). On the idea of a new articial intelligence based optimization algorithm inspired from the nature of vortex. BRAIN. Broad Research in Articial Intelligence and Neuroscience, 5 , 60-66.
Kose, U., & Arslan, A. (2017a). Forecasting chaotic time series via ans supported by vortex optimization algorithm: Applications on electroencephalogram time series. Arabian Journal for Science and Engineering, 42 , 3103-3114.
Kose, U., & Arslan, A. (2017b). Optimization of self-learning in computer engineering courses: An intelligent software system supported by articial neural network and vortex optimization algorithm. Computer Applications in Engineering Education, 25 , 142-156.
Lee, Y., & Lee, C.-K. (2003). Classication of multiple cancer types by multicategory support vector machines using gene expression data. Bioinformatics, 19 , 1132-1139.
Lisboa, P. J., & Taktak, A. F. (2006). The use of articial neural networks in decision support in cancer: a systematic review. Neural networks, 19, 408-415.
Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (2013). Machine learning: An articial intelligence approach. Springer Science & Business Media.
Miller, A., Blott, B. et al. (1992). Review of neural network applications in medical imaging and signal processing. Medical and Biological Engineering and Computing, 30 , 449-464.
Moavenian, M., & Khorrami, H. (2010). A qualitative comparison of articial neural networks and support vector machines in ecg arrhythmias classication. Expert Systems with Applications, 37 , 3088-3093.
Mohanty, P. K., & Parhi, D. R. (2015). A new hybrid optimization algorithm for multiple mobile robots navigation based on the cs-ans approach. Memetic Computing, 7 , 255-273.
Nabiyev, V. V. (2005). Articial intelligence: problems-methods-algorithms (In Turkish). Seckin Press.
Nilsson, N. J. (2014). Principles of articial intelligence. Morgan Kaufmann.
Ozbay, Y., & Karlik, B. (2001). A recognition of ecg arrhytihemias using articial neural networks. In Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE (pp. 1680-1683). IEEE volume 2.
Ozkan, A. O., Kara, S., Salli, A., Sakarya, M. E., & Gunes, S. (2010). Medical diagnosis of rheumatoid arthritis disease from right and left hand ulnar artery doppler signals using adaptive network based fuzzy inference system (ans) and music method. Advances in Engineering Software, 41 , 1295-1301.
Panapakidis, I. P., & Dagoumas, A. S. (2017). Day-ahead natural gas demand forecasting based on the combination of wavelet transform and anfis-genetic
algorithm-neural network model. Energy, 118 , 231-245.
Patterson, D. W. (1998). Articial neural networks: theory and applications. Prentice Hall PTR.
Polat, K., & Gunes, S. (2007a). Breast cancer diagnosis using least square support vector machine. Digital signal processing, 17 , 694-701.
Polat, K., & Gunes, S. (2007b). An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. Digital Signal Processing, 17 , 702-710.
Polat, K., Gunes, S., & Arslan, A. (2008). A cascade learning system for classication of diabetes disease: Generalized discriminant analysis and least square support vector machine. Expert systems with applications, 34 , 482-487.
Russell, S. J., & Norvig, P. (2016). Articial intelligence: a modern approach. Malaysia; Pearson Education Limited,.
Sahambi, J., Tandon, S., & Bhatt, R. (1997). Using wavelet transforms for ecg characterization. an on-line digital signal processing system. IEEE Engineering in Medicine and Biology Magazine, 16 , 77-83.
Sharaf-El-Deen, D. A., Moawad, I. F., & Khalifa, M. (2014). A new hybrid casebased reasoning approach for medical diagnosis systems. Journal of medical systems, 38 , 9.
Shoorehdeli, M. A., Teshnehlab, M., & Sedigh, A. K. (2009a). Training anfis as an identier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended kalman lter. Fuzzy Sets and Systems, 160, 922-948.
Shoorehdeli, M. A., Teshnehlab, M., Sedigh, A. K., & Khanesar, M. A. (2009b). Identication using ans with intelligent hybrid stable learning algorithm approaches
and stability analysis of training methods. Applied Soft Computing, 9 , 833-850.
Silipo, R., & Marchesi, C. (1998). Articial neural networks for automatic ecg analysis. IEEE transactions on signal processing, 46 , 1417-1425.
Snow, P. B., Smith, D. S., & Catalona, W. J. (1994). Articial neural networks in the diagnosis and prognosis of prostate cancer: a pilot study. The Journal of urology, 152 , 1923-1926.
Soni, J., Ansari, U., Sharma, D., & Soni, S. (2011). Predictive data mining for medical diagnosis: An overview of heart disease prediction. International Journal of Computer Applications, 17 , 43-48.
Srinivasan, V., Eswaran, C., & Sriraam, N. (2007). Approximate entropy-based epileptic eeg detection using articial neural networks. IEEE Transactions on information Technology in Biomedicine, 11 , 288-295.
Stoitsis, J., Valavanis, I., Mougiakakou, S. G., Golemati, S., Nikita, A., & Nikita, K. S. (2006). Computer aided diagnosis based on medical image processing and articial intelligence methods. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment , 569, 591-595.
Storn, R., & Price, K. (1997). Dierential evolution{a simple and ecient heuristic for global optimization over continuous spaces. Journal of global optimization, 11 , 341-359.
Ubeyli, E. D. (2009). Adaptive neuro-fuzzy inference system for classication of ecg signals using lyapunov exponents. Computer methods and programs in biomedicine, 93 , 313-321.
Vuckovic, A., Radivojevic, V., Chen, A. C., & Popovic, D. (2002). Automatic recognition of alertness and drowsiness from eeg by an articial neural network. Medical engineering & physics, 24 , 349-360.
Walia, N., Singh, H., & Sharma, A. (2015). Ans: Adaptive neuro-fuzzy inference system-a survey. International Journal of Computer Applications, 123.
Wu, Y., Giger, M. L., Doi, K., Vyborny, C. J., Schmidt, R. A., & Metz, C. E. (1993). Articial neural networks in mammography: application to decision making in the diagnosis of breast cancer. Radiology, 187 , 81-87.
Yen, J., & Langari, R. (1999). Fuzzy logic: intelligence, control, and information volume 1. Prentice Hall Upper Saddle River, NJ.