Open Access

ARTICLE

Multi-Label Machine Learning Classification of Cardiovascular Diseases

Chih-Ta Yen^1,*, Jung-Ren Wong², Chia-Hsang Chang²

1 Graduate Institute of Automation and Control, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan
2 Department of Electrical Engineering, National Taiwan Ocean University, Keelung City, 202301, Taiwan

* Corresponding Author: Chih-Ta Yen. Email:

(This article belongs to the Special Issue: Selected Papers from the International Multi-Conference on Engineering and Technology Innovation 2024 (IMETI2024))

Computers, Materials & Continua 2025, 84(1), 347-363. https://doi.org/10.32604/cmc.2025.063389

Received 13 January 2025; Accepted 24 April 2025; Issue published 09 June 2025

Abstract

In its 2023 global health statistics, the World Health Organization noted that noncommunicable diseases (NCDs) remain the leading cause of disease burden worldwide, with cardiovascular diseases (CVDs) resulting in more deaths than the three other major NCDs combined. In this study, we developed a method that can comprehensively detect which CVDs are present in a patient. Specifically, we propose a multi-label classification method that utilizes photoplethysmography (PPG) signals and physiological characteristics from public datasets to classify four types of CVDs and related conditions: hypertension, diabetes, cerebral infarction, and cerebrovascular disease. Our approach to multi-disease classification of cardiovascular diseases (CVDs) using PPG signals achieves the highest classification performance when encompassing the broadest range of disease categories, thereby offering a more comprehensive assessment of human health. We employ a multi-label classification strategy to simultaneously predict the presence or absence of multiple diseases. Specifically, we first apply the Savitzky-Golay (S-G) filter to the PPG signals to reduce noise and then transform into statistical features. We integrate processed PPG signals with individual physiological features as a multimodal input, thereby expanding the learned feature space. Notably, even with a simple machine learning method, this approach can achieve relatively high accuracy. The proposed method achieved a maximum F1-score of 0.91, minimum Hamming loss of 0.04, and an accuracy of 0.95. Thus, our method represents an effective and rapid solution for detecting multiple diseases simultaneously, which is beneficial for comprehensively managing CVDs.

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Keywords

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