Prediction of Heart Disease Severity Using Hierarchically-Structured Machine-Learning Models with Feature Space Reduction

Ayami Kiuchi, Tomoya Fujita, Hayato Yamana

2024

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.

Paper Citation

in Harvard Style

Kiuchi A., Fujita T. and Yamana H. (2024). Prediction of Heart Disease Severity Using Hierarchically-Structured Machine-Learning Models with Feature Space Reduction. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-688-0, SciTePress, pages 662-670. DOI: 10.5220/0012436700003657

in Bibtex Style

@conference{healthinf24,
author={Ayami Kiuchi and Tomoya Fujita and Hayato Yamana},
title={Prediction of Heart Disease Severity Using Hierarchically-Structured Machine-Learning Models with Feature Space Reduction},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2024},
pages={662-670},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012436700003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - Prediction of Heart Disease Severity Using Hierarchically-Structured Machine-Learning Models with Feature Space Reduction
SN - 978-989-758-688-0
AU - Kiuchi A.
AU - Fujita T.
AU - Yamana H.
PY - 2024
SP - 662
EP - 670
DO - 10.5220/0012436700003657
PB - SciTePress