Prediction of Heart Disease using SVM
Nagaraj M. Lutimath1, Arathi B N2, Shona M3
1Nagaraj M. Lutimath, Department of Computer Science and Engineering, Sri Venkateshwara College of Engineering, Bengaluru (Karnataka), India.
2Arathi B N, Department of Computer Science and Engineering, Sri Venkateshwara College of Engineering, Bengaluru (Karnataka), India.
3Shona M, Department of Computer Science and Engineering, Sri Venkateshwara College of Engineering, Bengaluru (Karnataka), India.
Manuscript received on 20 August 2019 | Revised Manuscript received on 30 August 2019 | Manuscript Published on 16 September 2019 | PP: 486-489 | Volume-8 Issue-2S6 July 2019 | Retrieval Number: B10920782S619/2019©BEIESP | DOI: 10.35940/ijrte.B1092.0782S619
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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: Support Vector Machine (SVM) is an important classification method in data mining. It is a supervised classification technique. It finds a hyperplane for classification of the target classes. The heart disease consists set of disorders affecting the heart. It includes blood vessel problems such as irregular heart beat issues, weak heart muscles, congenital heart defects, cardio vascular disease and coronary artery disease. Coronary heart disorder is a familiar type of heart disease. It reduces the blood flow to the heart leading to a heart attack. In this paper the UCI machine learning repository data set consisting of patients suffering from heart disease is analyzed using support vector machines. The classification accuracy of the patients suffering from heart disease is predicted. Implementation is done using R language.
Keywords: Support Vector Machines, UCI Machine Learning Repository Data Set, Data Mining, R Studio.
Scope of the Article: Materials Science (all)