Intelligent Diagnosis of Cardiac Disease Prediction using Machine Learning
Ravindhar NV1, Anand2, Hariharan Shanmugasundaram3, Ragavendran4, Godfrey Winster5

1NV Ravindhar, received his B.E, M.E amd Ph.D degree specialized in Computer Science and Engineering. Currently he is pursuing his Ph.D degree from Saveetha University, Chennai, India.
2Anand, Saveetha Engineering College and currently working in Saveeetha Engineering College, India.
3Hariharan shanmugasundaram, is with Saveetha Engineering College and currently working as Professor in Saveeetha Engineering College, India.
4Ragavendran completed undergraduate B.E(CSE) programme in Saveetha Engineering College affiliated to Anna University, Chennai, India.
5Godfrey Winster, is currently working as Professor in Computer Science and Engineering department at Saveetha Engineering College, Chennai, India.

Manuscript received on 26 August 2019. | Revised Manuscript received on 09 September 2019. | Manuscript published on 30 September 2019. | PP: 1417-1421 | Volume-8 Issue-11, September 2019. | Retrieval Number: J97650881019/2019©BEIESP | DOI: 10.35940/ijitee.J9765.0981119
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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: Cardiac disease have become worldwide common public health issue, mainly due to lack of awareness of health, poor lifestyle and poor consumption. Practitioners may have different concerns when it comes to disease diagnosis, which result in different decisions and actions. On the other hand, even in the specific case of a typical disease the amount of information available is so massive that it can be difficult to make accurate and reliable decisions. With adequate patient and non-patient medical constraints, it is possible to accurately predict how likely it is that a person with heart disease and to obtain potential information from these systems. A mechanized framework for therapeutic analysis would also dramatically increase medical considerations and reduce costs. We developed a framework in this exploration that can understand the principles of predicting the risk profile of patients with the clinical data parameters. In this article, four machine learning algorithms and one neural network algorithm were used to compare performance measurements to cardiac diseases identification. We evaluated the algorithms with respect to accuracy, precision, recall and F1 settings to achieve the ability to predict cardiac attacks. The results show our method achieved 98 percent accuracy by neural network algorithm to predict cardiac diseases.
Keywords: Human Cardiac, Disease, Machine Learning, Prediction, Heart disease prediction System, CVD,Neural Network.
Scope of the Article: