Journal of Software Engineering & Intelligent Systems

ISSN: 2518-8739 (Online)
DECEMBER 2019 | VOLUME. 4  ISSUE. 3
Title:

A SUPPORT VECTOR MACHINE BASED HEART DISEASE PREDICTION

Authors:

Tsehay Admassu Assegie

Abstract:

Disease classification problem can be solved by building machine learning models by training a machine to identify disease classes. The classification of disease is achieved by using machine learning algorithm like Support Vector machine (SVM). A SVM is a machine learning approach in which the machine uses predefined labels from the known set to determine or predict new classes of disease which has never seen before. In this paper, we used the standard kaggle heart disease dataset for classification of heart disease using a SVM learning algorithm. Finally, the accuracy of SVM is evaluated and the evaluation result shows 73.41% accuracy on heart disease classification.

Keywords: machine learning; heart disease classification; support vector machine; heart disease; prediction;
Available: A SUPPORT VECTOR MACHINE BASED HEART DISEASE PREDICTION by Tsehay Admassu Assegie will be available under cc by-nc 4.0 License on 31st December 2019. Permissions beyond the scope of this license may be available at JSEIS.

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