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Research Article

JCR. 2020; 7(14): 2060-2068


CARDIAC ARRHYTHMIA DATASET CLASSIFICATION USING XGBOOST GBM MODEL

S.Durga, Esther Daniel, T.Mary Neebha.

Abstract
The country's most common heart problem is a cardiac arrhythmia, in which the heart beats
sporadically. Research shows that in addition to the aging factor, stress and unhealthy lifestyle are the main
causes of heart disease. This paper aims at identifying arrhythmia and classifying it into 16 different variants.
The arrhythmia database provided at kaggle includes 279 properties, of which 206 are listed linearly and the
remainder are nominal. The new approach combining the 279 features into 50 features have been employed with
the XGBoost GSM classification model. The performance evaluation shows that the proposed model provides
improvement on accuracy over the state of art models

Key words: cardiac arrhythmia; machine learning; accuracy; feature transformation; classification; XGBoost



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