Journal Vol – 14 No -4, August 2019

Abstract:

Nowadays, artificial intelligence systems become actively used for the identification of different diseases using their medical data. Most of existing traditional medical systems are based on the knowledge of experts-doctors. In this thesis, the application of soft computing elements is considered to automate the process of diagnosing diseases, in particularly diagnosing of a heart attack. The research work will offer probable help to the medical practitioners and healthcare sector in making instantaneous resolution during the diagnosis of the diseases. The intelligent system will predict heart attacks from the patient dataset utilizing algorithms and help doctors in making diagnose of these illnesses. In this study, three techniques such as a neural network (back propagation), Fuzzy Inference System (FIS) and Adaptative Neuro-Fuzzy System (ANFIS) are considered for the design of the prediction system. The systems are designed using data sets. The data sets contain 1319 samples that includes 8 input attributes and one output. The output refers presence of a heart attack in the patient. For comparative analysis, the simulation results of the ANFIS model is compared with the simulation results of the neural network-based prediction model. The ANFIS model has shown better performance and outperformed NN based model. The obtained simulation results demonstrate the efficiency of using ANFIS model in the identification of heart attacks.

Keywords:

Artificial neural network,adaptive neuro-fuzzy inference system,fuzzy inference System (FIS),neural network (back propagation),heart attack,

Refference:

I. Adeli, A., &Neshat, M. (2010, March). A fuzzy expert system for heart disease
diagnosis. In Proceedings of International Multi Conference of Engineers and
Computer Scientists, Hong Kong (Vol. 1).
II. American Heart Association (2015). Heart.org/answers by heartNational
Center7272.Greenville.Ave.Dallas, TX 75231Customer Service
1-800-AHA-USA-11-800-242-8721 from https://www.heart.org/en/healthtopics/
consumer-healthcare/answers-by-heart-fact-sheets/answers-by-heart-factsheets-
lifestyle-and-risk-reduction /
III. Feshki, M. G., &Shijani, O. S. (2016, April). Improving the heart disease
diagnosis by evolutionary algorithm of PSO and Feed Forward Neural Network.
In Artificial Intelligence and Robotics (IRANOPEN), 2016 (pp. 48-53). IEEE.
IV. Haykin S (2009) Neural network and machine learning.3rd edCopyright © 2009
by Pearson Education, Inc., Upper Saddle River, New Jersey 07458. Pearson
Prentice Hall, New York ISBN-13: 978-0-13-147139-9 ISBN-10: 0-13-147139-2.
V. Teng, H., Liu, X., Liu, A., Shen, H., Huang, C., & Wang, T. (2018). Adaptive
transmission power control for reliable data forwarding in sensor based
networks. Wireless Communications and Mobile Computing, 2018.
VI. Narcisse, M. R., Rowland, B., Long, C. R., Felix, H., &McElfish, P. A. (2019).
Heart Attack and Stroke Symptoms Knowledge of Native Hawaiians and Pacific
Islanders in the United States: Findings From the National Health Interview
Survey. Health promotion practice, 1524839919845669.

VII. Takdastan, A., Mirzabeygi, M., Yousefi, M., Abbasnia, A., Khodadadia, R.,
Soleimani, H., …&Naghan, D. J. (2018). Neuro-fuzzy inference system
Prediction of stability indices and Sodium absorption ratio in Lordegan rural
drinking water resources in west Iran. Data in brief, 18, 255-261.
VIII. Nicole, O., Bell, D. M., Leste-Lasserre, T., Doat, H., Guillemot, F., &Pacary, E.
(2018). CaMKIIβ regulates nucleus-centrosome coupling in locomoting neurons
of the developing cerebral cortex. Molecular psychiatry, 23(11), 2111.
IX. Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE
transactions on systems, man, and cybernetics, 23(3), 665-685.
X. Kaya, E., Oran, B., &Arslan, A. (2011). A diagnostic fuzzy rule-based system for
congenital heart disease. World academy of science, Engineering and
technology, 78, 253-256.
XI. Patil, S. B., &Kumaraswamy, Y. S. (2009). Intelligent and effective heart attack
prediction system using data mining and artificial neural network. European
Journal of Scientific Research, 31(4), 642-656.
XII. Shanthi. S (February 2017). Customized Prediction of Heart Disease with
Adaptive Neuro Fuzzy Inference System International Journal of Advanced
Research in Computer and Communication Engineering, SO 3297:2007 Certified
XIII. Shinde, P. P., Oza, K. S., &Kamat, R. K (2016). An Analysis of Data Mining
Techniques in Aggregation with Real Time Dataset for the Prediction of Heart
Disease. InternationalJournal of Control Theory and Applications, 9(20), 327-336.
XIV. Sundar, N. A., Latha, P. P., & Chandra, M. R. (2012). Performance analysis of
classification data mining techniques over heart disease database. IJESAT]
International Journal of engineering science & advanced technology ISSN,
2250-3676.
XV. Vipul, A. S. (2009). Adaptive Neuro-Fuzzy Inference System for Effect of Wall
Capacitance in a Batch Reactor. Advances in Fuzzy Mathematics ISSN, 69-75.
XVI. WHO (2015). Cardiovascular diseases. Retrieved August 28, 2015 from
http://www.who.int/ media centre/factsheets/fs317/en /.
XVII. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353
XVIII. Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex
systems and decision processes. IEEE Transactions on systems, Man, and
Cybernetics, (1), 28-44.

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