Coronary Heart Disease detection and diagnosis using Artificial Neural Network

  • Seema Rani Research Scholar, University College of Engineering, Punjabi University, Patiala, Punjab, India,
  • Rajbir Kaur Assistant Professor, University College of Engineering, Punjabi University, Patiala, Punjab, India
  • Amit Wason Principal, Ambala College of Engineering and Applied Research, Ambala, Haryana, India
Keywords: Artificial Neural Networks, Coronary Heart Disease, and Mean Square Error


Every year we see that the expiry percentage increases as an outcome of heart syndrome. There are many factors which lead to this increase. Diagnosis of this disease should be perfect so as to reduce this increase; whereas, a misdiagnosis by the doctors or lack of knowledge in the patient may lead to an increase in expiry percentage. This blood coagulates totally lumps the veins, the heart muscle becomes “ravenous” for oxygen. Within a small duration of time, a decrease of heart muscle cells occurs, triggering lasting injury. This is cardiac disease. This paper considered various factors for the identification of heart disease, the problems, and the cures for the same. An intelligent system has been implemented which can be used to diagnose heart diseases. The developed system may be used to avoid the wrong judgment which is the major fault that may happen by physicians. For the implementation of the intelligent system, we have used the data of stat log for heart disease. The database consists of characteristics of persons being spotted for cardiac problems. The finding was used to approve a patient having cardiac disorder or not. We have used eleven different clinical datasets for this test. The collected data were used for three activities, first for the training of the network, second was validation followed by testing of network with data for which it was trained. The smart system was demonstrated on three different techniques, Feed Forward Back Propagation, Cascade Forward and Layer Recurrent model. While doing this study, we observed a performance of 74.55 and 87.935 for cascade forward model and Feed Forward with Back Propagation model respectively. From this experimentation about the identification of coronary heart syndrome, from the experiment it is revealed that the best prototype is Feed Forward with Back Propagation among all three tested prototypes.


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