Deep learning network for identification of Ischemia using clinical data
Varun Sapra1, Madan Lal Saini2
1Varun Sapra, Department of Computer Science, Jagannath University, Jaipur (R.J), India.
2Dr. Madan Lal Saini, Department of Computer Science, Jagannath University, Jaipur (R.J), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 2357-2363 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7282068519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Ischemic heart disease is amongst the foremost reasons of death and disability majorly because of atherosclerosis and other cardiovascular syndromes like cerebrovascular accidents and myocardial infarction. Ischemia can be diagnosed by using invasive & non-invasive methods. Invasive methods are generally expensive and always requires high level of technical and medical expertise. This paper focuses on a bio inspired optimization approach for the identification of effective biomarkers and deep learning based neural network technique on non-invasive clinical parameters to diagnose Ischemia with more accuracy. For experiment purpose, the clinical data of Coronary Artery disease (CAD) patients was collected from the cardiology department of Medical College, Shimla, India. The proposed method improves the prediction accuracy of Ischemia.
Keywords: Neural Network, Ischemia Heart Disease, Non-Invasive, Angiography
Scope of the Article: Deep learning