Prediction of Heart Disease using Machine Learning
Nagaraj M. Lutimath1, Chethan C2, Basavaraj S Pol3
1Nagaraj M. Lutimath, Department of Computer Science and Engineering, Sri Venkateshwara College of Engineering, Bengaluru (Karnataka), India.
2Chethan C, Department of Information Science and Engineering, Sri Venkateshwara College of Engineering, Bengaluru (Karnataka), India.
3Basavaraj S Pol, Department of Computer Science and Engineering, R L. Jalappa College of Engineering, Doddaballapur (Karnataka), India.
Manuscript received on 19 September 2019 | Revised Manuscript received on 06 October 2019 | Manuscript Published on 11 October 2019 | PP: 474-477 | Volume-8 Issue-2S10 September 2019 | Retrieval Number: B10810982S1019/2019©BEIESP | DOI: 10.35940/ijrte.B1081.0982S1019
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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: Machine learning is one of the fast growing aspect in current world. Machine learning (ML) and Artificial Neural Network (ANN) are helpful in detection and diagnosis of various heart diseases. Naïve Bayes Classification is a vital approach of classification in machine learning. The heart disease consists of set of range 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 Naïve Bayes classification and support vector machines. The classification accuracy of the patients suffering from heart disease is predicted using Naïve Bayes classification and support vector machines. Implementation is done using R language.
Keywords: Naïve Bayes Classification, Support Vector Machines, UCI Machine Learning Repository Data Set, R Studio.
Scope of the Article: Machine Learning