Heart Attack Prediction System Based Neural Arbitration

Heart Attack Prediction System Based Neural Arbitration

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

___

  • 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.