Authors:
Ayami Kiuchi
1
;
Tomoya Fujita
2
and
Hayato Yamana
3
Affiliations:
1
Department of Computer Science and Engineering, Waseda University, Tokyo, Japan
;
2
Department of Computer Science and Communications Engineering, Waseda University, Tokyo, Japan
;
3
Faculty of Science and Engineering, Waseda University, Tokyo, Japan
Keyword(s):
Heart Disease, Hierarchical Prediction, Feature Selection, Feature Space Reduction, mRMR, SMOTE.
Abstract:
Heart disease is the primary cause of death worldwide according to the 2019 statistics published by the World Health Organization (WHO), with roughly 8.9 million people dying annually. Predicting the likelihood and severity of this disease leads to earlier detection and helps reduce the workload of medical professionals. Previous studies have adopted a one-time classification that is insufficient to predict heart disease severity. This study proposes a novel classification method to enhance the prediction accuracy of heart disease by using: 1) a hierarchical binary-classification technique to classify the severity in order from the lowest level and 2) a data-preprocessing technique to transform continuous values into binary values based on medical knowledge and statistics information to decrease the feature space. An experimental evaluation of the heart-disease dataset from the UC Irvine (UCI) machine-learning repository confirms that the proposed method achieves the highest accuracy
at 100% in predicting the presence of heart disease and at 93.13% in its severity level. In addition, the proposed method achieved 96.67%, 91.25%, 90.59%, and 93.64% accuracy for severity prediction in the Cleveland, Hungarian, Long-Beach-VA, and Switzerland datasets, respectively.
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