Predicting the Symptoms of Cardio Vascular Disease using Machine Learning Technique
K. Arunmozhi Arasan1, E. Ramaraj2, S. Muthukumaran3

1K.Arunmozhi Arasan, Research Scholar, Department of Computer Science, Alagappa University, Karaikudi, India.
2E.Ramaraj, Professor and Head, Department of Computer Science, Alagappa Univeristy, Karaikudi, India.
3S. Muthukumaran, Research Scholar, Department of Computer Science, Alagappa University, Karaikudi, India.

Manuscript received on 29 August 2019. | Revised Manuscript received on 06 September 2019. | Manuscript published on 30 September 2019. | PP: 1584-1587 | Volume-8 Issue-11, September 2019. | Retrieval Number: K18650981119/2019©BEIESP | DOI: 10.35940/ijitee.K1865.0981119
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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: Heart disease is the top most reason for mortality in India. Cardio Vascular Disease is a type of heart disease and it refers to narrowed or blocked blood artery vessels that affect the flow of blood to and from the heart and rest of the parts of the human body. Globally 31% of the people died due to Cardio Vascular Disease per year. Blood pressure, diabetes, abnormal cholesterol increase, diabetes, hypertension are the risk factors for the Cardio Vascular Disease. This paper used a machine learning approach with C4.5 classifier to predict the reason for the Cardio Vascular Disease. The data used for this research was collected from private hospitals located in different districts of Tamil Nadu. The parameters which cause Cardio Vascular Disease are consulted with doctors and a dataset was formed with 18 parameters. Decision tree was obtained from the dataset using C4.5 algorithm and the reason for the cause of Cardio Vascular Disease was generated as rules from the Decision Tree. This extracted knowledge is used to predict the reason for Cardio Vascular Disease and prevents deaths due to heart disease.
Keywords: Machine Learning, Cardio Vascular Disease, Decision Tree, C4.5.
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