Heart Disease Prediction Using Machine Learning Algorithm
Praveen Kumar Reddy M1, T Sunil Kumar Reddy2, S. Balakrishnan3, Syed Muzamil Basha4, Ravi Kumar Poluru5
1Praveen Kumar Reddy M, Assistant Professor in School of Information Technology and Engineering, VIT University, Vellore, Tamil Nadu, India.
2T Sunil Kumar Reddy, Professor in the Department of Computer Science Engineering, Sri Venkateswara College of Engineering and Technology, Chittoor.
3S. Balakrishnan, Professor in Computer Science and Business Systems, Sri Krishna college of Engineering and Technology.
4Syed Muzamil Basha, Ph.D. degree in School of computer science and Engineering from the VIT (Deemed by university), Vellore, India.
5Ravi Kumar Poluru, Research Scholar in the School of Computer Science & Engineering, Vellore Institute of Technology, Vellore.
Manuscript received on 15 July 2019 | Revised Manuscript received on 21 July 2019 | Manuscript published on 30 August 2019 | PP: 2603-2606 | Volume-8 Issue-10, August 2019 | Retrieval Number: J93400881019/19©BEIESP | DOI: 10.35940/ijitee.J9340.0881019
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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 a common problem which can be very severe in old ages and also in people not having a healthy lifestyle. With regular check-up and diagnosis in addition to maintaining a decent eating habit can prevent it to some extent. In this paper we have tried to implement the most sought after and important machine learning algorithm to predict the heart disease in a patient. The decision tree classifier is implemented based on the symptoms which are specifically the attributes required for the purpose of prediction. Using the decision tree algorithm, we will be able to identify those attributes which are the best one that will lead us to a better prediction of the datasets. The decision tree algorithm works in a way where it tries to solve the problem by the help of tree representation. Here each internal node of the tree represents an attribute, and each leaf node corresponds to a class label. The support vector machine algorithm helps us to classify the datasets on the basis of kernel and it also groups the dataset using hyperplane. The main objective of this project is to try and reduce the number of occurrences of the heart diseases in patients.
Keywords: About Four Key Words or Phrases in Alphabetical Order, Separated by Commas.
Scope of the Article: Machine Learning