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Heart Attack Prediction System Based Neural Arbitration

Year 2015, Volume: 5 Issue: 2, 32 - 39, 23.07.2016

Abstract

Heart attack is an asymptomatic and epidemic medical condition that may suddenly occur and causes “death”. Therefore, it is a life-threatening condition and it should be detected before it occurs. Heart attack is so far predicted using the conventional ways of doctor’s examination and by performing some medical tests such as stress test, ECG, and heart CTScan etc. The coronary vessels constriction, the cholesterol levels in the arteries, and other attributes can be good indicators for making effective decisions. In this paper, a neural network based support decision system is developed for the prediction of heart attack. The proposed system uses 14 medical attributes, obtained from the Cleveland database such as sex, heart rate, and vessels narrowing etc. Two attributes have been emphasized in order to distinguish the heart attack from other heart diseases; the vessels constriction rate and the chest pain type. The testing results show high efficiency and capability for the designed system to predict heart attack and diagnose the three medical conditions: normal, abnormal, and imminent to heart attack

References

  • Centre for Heart Disease Control and Prediction. Retrieved from http://www.cdc.gov/heartdisease/facts.htm
  • Nabeel Al-Milli, 2013. A backpropogation neural network for prediction of heart disease. In Journal of Theoretical and Applied Information Technology, vol.56, no.1, pp. 131-135.
  • Dr. K. Usha Rani, 2011. Analysis of heart diseases dataset using neural network approach. In International Journal of Data Mining & Knowledge Management Process (IJDKP), vol.1, no.5, pp. 1-8.
  • Miss. Chaitrali S. Dangare, Dr. Mrs. Sulabha S. Apte, 2012. A data mining approach for prediction of heart disease using neural networks. In International Journal of Computer Engineering and Technology (IJCET), Vol. 3, Issue 3, pp. 30-40.
  • Dilip Roy Chowdhury, Mridula Chatterjee R.K. Samanta, 2011. An Artificial Neural Network Model for Neonatal Disease Diagnosis. In International Journal of Artificial Intelligence and Expert Systems (IJAE), vol. 2, Issue 3, pp. 96-106.
  • Adnan Khashman, Credit risk evaluation using neural networks: Emotional versus conventional Models. In Elsevier, 2011.
  • K. Anil Jain, Jianchang Mao and K.M. Mohiuddi, 1996. Artificial Neural Networks: A Tutorial, IEEE Computers, pp.31-44.
  • R. Rojas, (1996). Neural Networks: a systematic introduction, Springer-Verlag.Davis
  • Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
Year 2015, Volume: 5 Issue: 2, 32 - 39, 23.07.2016

Abstract

References

  • Centre for Heart Disease Control and Prediction. Retrieved from http://www.cdc.gov/heartdisease/facts.htm
  • Nabeel Al-Milli, 2013. A backpropogation neural network for prediction of heart disease. In Journal of Theoretical and Applied Information Technology, vol.56, no.1, pp. 131-135.
  • Dr. K. Usha Rani, 2011. Analysis of heart diseases dataset using neural network approach. In International Journal of Data Mining & Knowledge Management Process (IJDKP), vol.1, no.5, pp. 1-8.
  • Miss. Chaitrali S. Dangare, Dr. Mrs. Sulabha S. Apte, 2012. A data mining approach for prediction of heart disease using neural networks. In International Journal of Computer Engineering and Technology (IJCET), Vol. 3, Issue 3, pp. 30-40.
  • Dilip Roy Chowdhury, Mridula Chatterjee R.K. Samanta, 2011. An Artificial Neural Network Model for Neonatal Disease Diagnosis. In International Journal of Artificial Intelligence and Expert Systems (IJAE), vol. 2, Issue 3, pp. 96-106.
  • Adnan Khashman, Credit risk evaluation using neural networks: Emotional versus conventional Models. In Elsevier, 2011.
  • K. Anil Jain, Jianchang Mao and K.M. Mohiuddi, 1996. Artificial Neural Networks: A Tutorial, IEEE Computers, pp.31-44.
  • R. Rojas, (1996). Neural Networks: a systematic introduction, Springer-Verlag.Davis
  • Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
There are 9 citations in total.

Details

Other ID JA56EJ94SA
Journal Section Articles
Authors

Abdulkader Helwan This is me

Publication Date July 23, 2016
Published in Issue Year 2015 Volume: 5 Issue: 2

Cite

APA Helwan, A. (2016). Heart Attack Prediction System Based Neural Arbitration. TOJSAT, 5(2), 32-39.
AMA Helwan A. Heart Attack Prediction System Based Neural Arbitration. TOJSAT. July 2016;5(2):32-39.
Chicago Helwan, Abdulkader. “Heart Attack Prediction System Based Neural Arbitration”. TOJSAT 5, no. 2 (July 2016): 32-39.
EndNote Helwan A (July 1, 2016) Heart Attack Prediction System Based Neural Arbitration. TOJSAT 5 2 32–39.
IEEE A. Helwan, “Heart Attack Prediction System Based Neural Arbitration”, TOJSAT, vol. 5, no. 2, pp. 32–39, 2016.
ISNAD Helwan, Abdulkader. “Heart Attack Prediction System Based Neural Arbitration”. TOJSAT 5/2 (July 2016), 32-39.
JAMA Helwan A. Heart Attack Prediction System Based Neural Arbitration. TOJSAT. 2016;5:32–39.
MLA Helwan, Abdulkader. “Heart Attack Prediction System Based Neural Arbitration”. TOJSAT, vol. 5, no. 2, 2016, pp. 32-39.
Vancouver Helwan A. Heart Attack Prediction System Based Neural Arbitration. TOJSAT. 2016;5(2):32-9.