Intelligent Heart Disease Prediction using Neural Network
Soumonos Mukherjee1, Anshul Sharma2
1Soumonos Mukherjee, Department of Computer Science and Engineering VIT,Vellore, (Tamil Nadu), India.
2Anshul Sharma, Department of Computer Science and Engineering VIT,Vellore, (Tamil Nadu), India.
Manuscript received on 24 January 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 January 2019 | PP: 402-405 | Volume-7 Issue-6, March 2019 | Retrieval Number: E2095017519©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: Health-care is a field of the most needed service and an economically 2nd largest industry in 21st century. While we talk about the affordability and quality assurance in health-care industry, several statistical analysis is carried on to make health solutions more precise and flawless in this current era of increasing health problems and chronic diseases. Advancements on data driven intelligent technologies is disease diagnosis and detection, treatment and research are remarkable. Medical image analysis, symptom based disease prediction is the part where the most sought after brains are working. In this paper we aim to present our proposed model on the prediction on diagnosis of cardio vascular disease with ECG analysis and symptom based detection. The model aims to be researched and advance in further to become robust and end to end reliable research tool. We will discuss about the classical methods and algorithms implemented on CVD prediction, gradual advancements, draw comparison of performance among the existing systems and propose an enhanced multi-module system performing better in terms of accuracy and feasibility. Implementation , training and testing of the modules have been done on datasets obtained from UCI and Physionet data repositories. Data format have been modified in case of the ECG report data for betterment of action by the convolutional neural network used in our research and in the risk prediction module we have chosen attributes for training and implementing the multi-layered neural network developed by us. The further research and advancement possibilities are also mentioned in the paper.
Keywords: attribute, classification CVD(Cardio vascular disease), convolutional neural network, multi-layered neural network, Physionet, UCI (University of California,Irwin).
Scope of the Article: Classification