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PREDICTING HEART DISEASES BY USING MACHINE LEARNING METHODS

BROWSE_DETAIL_CREATION_DATE: 29-07-2016

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BROWSE_DETAIL_TYPE: Thesis

BROWSE_DETAIL_SUB_TYPE: Masters

BROWSE_DETAIL_PUBLISH_STATE: Unpublished

BROWSE_DETAIL_FORMAT: PDF Document

BROWSE_DETAIL_LANG: English

BROWSE_DETAIL_SUBJECTS: TECHNOLOGY, Computer applications to medicine. Medical informatics,

BROWSE_DETAIL_CREATORS: Benzreig, Ashraf M. Saied (Author),

BROWSE_DETAIL_CONTRIBUTERS: Özçelik, Erol (Thesis Advisor),

BROWSE_DETAIL_TAB_KEYWORDS

Artificial neural network (ANN), K- Nearest Neighbor (KNN), Support Vector Machine (SVM), Heart diseases


BROWSE_DETAIL_TAB_ABSTRACT

Heart diseases are ranked as number one cause of death in the world. The aim of this thesis is to find a robust method for predicting heart disease. A dataset obtained from the UCI machine learning warehouse consisting of 297 cases and 14 features with 2 classes of attributes was used. In this  thesis three different machine learning methods, namely Artificial Neural Network (ANN), Support Vector Machine (SVM) and K-nearest neighbor (KNN)  were used to predict heart disease. The best performance was obtained when ANN was used. The results have been discussed. 


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