Prediction of cardiovascular diseases by integrating multi-modal features with machine learning methods
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Integrating ECG with other bioelectrical data including phonocardiogram data has enabled improved performance of cardiovascular disease diagnosis.
“Integrating ECG with other bioelectrical data, including phonocardiogram (PCG) data, has enabled improved performance of cardiovascular disease diagnosis and would be promising for other biomedical applications [107,108].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [108]
Cites
Using AUC and accuracy in evaluating learning algorithms
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Integrated multimodal artificial intelligence framework for healthcare applications
Soenksen, L. R., Ma, Y., Zeng, C., Boussioux, L., Villalobos Carballo, K., Na, L., Wiberg, H. M., Li, M. L., Fuentes, I., & Bertsimas, D. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00689-4 · Cited by 380 · see citing works
“Recently, AI/ML models leveraging multiple data modalities have been demonstrated for the domains of cardiology 7 – 9 , dermatology 10 , gastroenterology 11 , gynecology 12 , hematology 13 , immunology 14 , nephrology 15 , neurology 16,17 , oncology 18 – 20 , ophthalmology 21 , psychiatry 22 ,…”
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“Li et al. 2021 [76] presented multi-modal machine learning techniques related to heart diseases detection and tracking, signal estimation, and HR estimation.”
BackgroundA comprehensive review of deep learning-based models for heart disease prediction
Zhou, C., Dai, P.-F., Hou, A., Zhang, Z.-X., Liu, L., Li, A., & Wang, F.-S. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10899-9 · Cited by 76 · see citing works
“In the realm of multi-modal HDP, Li et al. introduced a groundbreaking approach that capital-ized on the synergistic power of electrocardiogram (ECG) and phonocardiogram (PCG) data (Li et al. 2021).”
Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis
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“Integrating ECG with other bioelectrical data, including phonocardio-gram (PCG) data, has enabled improved performance of cardiovascular disease diagnosis and would be promising for other biomedical applications [107,108].”
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An improved hawks optimizer based learning algorithms for cardiovascular disease prediction
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Multi-Modality Cardiac Image Computing: A Survey
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MSGformer: A multi-scale grid transformer network for 12-lead ECG arrhythmia detection
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Cardiovascular disease detection based on deep learning and multi-modal data fusion
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“To our knowledge, only Li et al. have used ML models to process synchronized heart sound and ECG signals [ 158 ], and there is currently no DL diagnostic model based on synchronized heart sound and ECG signals.”
MethodAutomated Ensemble Multimodal Machine Learning for Healthcare
Imrie, F., Denner, S., Brunschwig, L. S., Maier-Hein, K. H., & van der Schaar, M. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3530156 · Cited by 45 · see citing works
“As a result, while multimodal ML remains in its infancy, models that incorporate multiple data modalities have been developed for several medical domains including cardiology [11], dermatology [12], oncology [13, 14], and radiology [15].”
BackgroundReview of Phonocardiogram Signal Analysis: Insights from the PhysioNet/CinC Challenge 2016 Database
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A review on deep learning methods for heart sound signal analysis
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“The computation details of the used metrics for the performance evaluation are listed by li et al.[16].”
Mandala, S., Amini, S. S., Adiwijaya, Syaifullah, A. R., Pramudyo, M., Nurmaini, S., & Abdullah, A. H. (2023) · IEEE Access · doi:10.1109/access.2023.3338853 · Cited by 31
“Cardiovascular diseases stand as the leading cause of global mortality, responsible for approximately 17.9 million deaths in 2017 [8].”
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“Apart from this, long short-term memory (LSTM) networks, which are capable of dealing with sequence prediction problems, have been used in conjunction with genetic algorithms (GAs) to minimize prediction errors [12].”
MethodAshika, T., & Grace, G. (2025) · Frontiers Digit. Health · doi:10.3389/fdgth.2025.1609308 · Cited by 21 · see citing works
Fusion Modeling; Combining Clinical and Imaging Data to Advance Cardiac Care
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“During the procedure, the digital twin can help guide the procedure, see Figure 5.”
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“Another challenge is to collaborate with physicians to get more useful data for improving the model (Li et al. 2021) by updating with new patient descriptions (Chen and Hengjinda 2021).”
BackgroundEnhancing Cardiovascular Disease Diagnosis: The Power of Optimized Ensemble Learning
Yazdi, F., & Asadi, S. (2025) · IEEE Access · doi:10.1109/access.2025.3550015 · Cited by 20 · see citing works
“Li et al. [25] proposed a multi-modal approach combining ECG and PCG features for cardiovascular disease prediction, achieving high performance.”
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Hilbert domain characterizations of wavelet packets for automated heart sound abnormality detection
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“For instance, to enhance the heart disease management system via multi-modal fusion, [42] applied PCA to reduce the dimensionality and genetic algorithm for classification.”
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“They used SVM as the classifier, and the AUC value of their model’s highest performance is 0.936 [24].”
MethodInfluentialZhou, Z.-Q., & Wang, J.-W. (2025) · Rambam Maimonides Medical Journal · doi:10.5041/rmmj.10558 · Cited by 12
“59 In terms of outcomes, some studies have linked wearable-derived metrics to hard events.”
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Explainable Multimodal Deep Learning for Heart Sounds and Electrocardiogram Classification
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“However, emerging approaches are now considering the integration of both PCG and ECG signals, marking a shift towards more comprehensive diagnostic methodologies [6][7].”
Hsieh, Y.-H., Liu, H.-K., Wu, J., Yan, T.-Y., Huang, Y., Guo, M., & Yang, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-04746-2 · Cited by 12 · see citing works
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“Wang et al. 17 26 demonstrated a multimodal deep learning approach for CVD risk predictionusing electronic health records.”
Cardiovascular Disease Detection Based on Multi-Modal Data Fusion and Multi-Branch Residual Network
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“Recently, several studies have begun to pay attention to the fusion of ECG and PCG for the diagnosis of cardiovascular diseases [10-12].”
BackgroundMethodInfluentialRan, G.-H., Wang, Y., Zhang, H., Cheng, J., & Lai, D. (2025) · Italian National Conference on Sensors · doi:10.3390/s25175263 · Cited by 7
“As a result, our model achieves a classification accuracy of 89.74% with an energy consumption of 209.6 µ J. Compared with [16], our method improves accuracy by approximately 2.44%, with only a 56 µ J increase in energy consumption.”
InfluentialLi, W.-P., Chuah, J. H., Tan, G., Liu, C., & Ting, H. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.3038 · Cited by 7 · see citing works
“Studies by Li, Hu & Liu (2021) and The experimental results in Table 8 highlight the effectiveness of PACFNet ’ s designed feature extraction and cross-modal fusion.”
End-to-End Multi-Modal Tiny-CNN for Cardiovascular Monitoring on Sensor Patches
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“We compare our model with the three previous works with the highest accuracy in literature that used a DL approach trained and evaluated on the same database, as well as made use of both the ECG and PCG data [8], [11], [14].”
MethodDeep Learning for Heart Sound Analysis: A Literature ReviewPreprint
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“proposed a multi-modal machine learning approach that combines simultaneous electrocardiogram and PCG signals to predict cardiovascular diseases [131].”
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“[9] In Asian countries, heart diseases are responsible for 22% of total mortality.”
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“This data-set can be downloaded from Zenodo [28].”
Survey on DL - Driven Multi-Modal Data in Cardiovascular Disease Prediction
Gawande, N., Goyal, D., & Sankhla, K. (2025) · 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS) · doi:10.1109/icmlas64557.2025.10968461 · Cited by 6 · see citing works
“Initially, the input ECG image will be acquired from the dataset and then the ECG signals will be transformed into ECG images by performing two steps, such as normalization and image transformation as in [26], wherein the normalization involves two steps, splitting and reshaping.”
Prediction of Cardiovascular Disease using XGBoost with OPTUNA
Jain, A., Singh, A., & Doherey, A. (2025) · SN Computer Science · doi:10.1007/s42979-025-03954-x · Cited by 5 · see citing works
“Li et al. [12], developed a model that used phonocardiogram (PCG) and Electrocardiogram (ECG) images to predict cardio-vascular disease.”
Artificial Intelligence in Medicine: Basic Knowledge, Applications, Ethics and Perspectives
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“By doing this, they provide medical practitioners the knowledge they need to make wise choices and diagnose illnesses with a previously unheard-of degree of accuracy[11].”
BackgroundHierarchical fusion of electrocardiogram and phonocardiogram data improves heart failure detection
Ma, F., Zhang, H., Fang, S., Wang, Q., Lin, F., Chao, L., Wang, Z., Li, Q., & Zhang, P. (2026) · Patterns · doi:10.1016/j.patter.2025.101448 · Cited by 3 · see citing works
“We reused the publicly available source code provided by Li et al. 29 and Zhang et al. 30 and reimplemented the other methods strictly following their papers.”
Zhang, P., Li, Z., Sang, R., Liu, Y., Cai, Y.-Q., Tan, Y., & Li, S.-C. (2025) · arXiv.org · doi:10.48550/arxiv.2505.18174 · Cited by 3
“The coupling signal captures the dynamic transformation between them. their complementary features to enhance diagnostic accuracy and reliability [6]–[8].”
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“This may be realized using RNN variations of DL, which can handle serial time-series information (Li, Hu, & Liu, 2021).”
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“However, recognizing limitations, including small datasets, lack of comparisons, and the opaque nature of certain models [2] [10] [11], emphasizes the imperative for future studies to prioritize explainable AI frameworks.”
BackgroundA Comprehensive Study on Non-Contact Heart Rate Prediction: Integrating ICA and PCA in rPPGNet
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“Li et al. (2021) introduced advanced multi-modal machine learning methodologies pertaining to cardiovascular conditions.”
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Heart sound classification based on equal scale frequency cepstral coefficients and deep learning
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Non - Contact Heart Rate Monitoring System using Deep Learning Techniques
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“Gudi et al.. [9] introduced multimodal machine learning algorithms for heart disease diagnosis in 2021.”
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“Li et al. [10] introduced a multimodal machine learning approach, which integrated electrocardiogram (ECG) and phonocardiogram (PCG) characteristics for cardiovascular disease forecasting.”
Contrastive Learning for Personalized Cardiovascular Risk Prediction from Multi-Lead ECG Signal
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“As a response to that, [19] proposed an end-to-end deep learning pipeline that used contrastive learning on multi modal representations to fuse representation across different data sources automatically.”
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Cardiovascular disease classification based on a multi-classification integrated model
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An integrated architecture for ECG classification combining state-space model Mamba with CNNs
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“Additionally, old eliminating of misalignment adds up to the drawbacks, since ECG is monitored around-the-clock, while imaging and EHR events take place intermittently thus demanding very sophisticated time-synchronization or sequence-modeling techniques [74, 81].”
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“Continuous monitoring is essential for early detection, and combining modalities such as electrocardiograms (ECGs) and phonocardiograms (PCGs) can enhance diagnostic performance [4].”
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“The existing techniques used to validate the developed model’s efficiency are DenseNet-201 [17], ANN [20], KNN [21], and CL-Net [36].”
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Resan: A Residual Dual‐Attention Network for Abnormal Cardiac Activity Detection
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“While several studies [4, 24, 43] used synchronized ECG and PCG signals, studies [24, 43] ignored the interaction between ECG and PCG.”
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“The PCG signal is a non-invasive technique that detects human heart conditions through the vibrations produced by muscle contractions and heart valve closures, which can be heard as distinct sounds and murmurs[6].”
BackgroundAn efficient hybrid XGBLR-IMBODE model for heart disease prediction
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“9 million people died from heart disease in 2019, accounting for 32% of all global deaths[1, 2].”
BackgroundStudy of Feature Extraction Method to Detect Myocardial Infraction Using a Phonocardiogram
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“In the following year, P. Li, Y. Hu, and Z. P. Liu [10] conducted an experiment on classification in detecting CAD using MFCC and KNN and using SVM for CAD categorization.”
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Xiao, T., & Che, L. (2026) · Biomedical engineering and physics express · doi:10.1088/2057-1976/ae8d2d
“As summarized in table 5, many comparative approaches (e.g. Li et al [33] and Kalatehjari et al [35]) rely strongly on complex preprocessing steps, denoted as ASP.”
Hybrid deep learning framework for cardiovascular risk prediction in post-COVID-19 patients
Juliet, S., Banumathi, J., & Singh, J. A. J. (2026) · Scientific Reports · doi:10.1038/s41598-026-57064-6
Xu, C., Song, X.-Y., Wang, S., Zhao, B., & Yan, J. (2026) · 2026 International Conference on Image, Signal Processing and Pattern Recognition (ISPP) · doi:10.1109/ispp69262.2026.11543110
“Bimodal ECG-PCG diagnosis has been widely shown to characterize cardiac pathology more comprehensively [2].”
Jayashree, S., Royal, G. K. S. R., & Pavan, C. (2026) · International Conferences on Information Science and System · doi:10.1109/iciss67859.2026.11453756
“Li et al. [4] integrated machine learning algorithms with multimodal aspects to predict cardiovascular diseases more precisely, but Chai et al. [5] and Heo et al. [6] focused on the multimodal data integration with a deep learning algorithm, which allows more effective cancer diagnosis and…”
Deep Belief Network for Predicting Heart Disease Using Wearable Sensor Data
Samandarova, G., Nailevich, I. R., Ruziyev, K., Subbarayudu, C. B. V., Janani Sri, S., & Younus, Y. (2025) · 2025 International Conference on AI-Driven STEM Education and Learning Technologies (AISTEMEDU) · doi:10.1109/aistemedu67077.2025.11403877
“Li et al. (2021) [32] made use of machine learning to identify the way of incorporating different kinds of clinical and physiological data.”
V.V.A.S.Lakshmi, Nettam, C. J., Veeravatnam, B., Kumar, P. V. S., & Mouli, K. C. (2025) · International Conference on Evolutionary Computing and Mobile Sustainable Networks (ICECMSN) · doi:10.1109/icecmsn68058.2025.11383044
Multimodal Learning with Intermediate Fusion for Enhanced Arrhythmia Classification
Matteo, M., Abed, M. T., & Zhao, X. (2025) · 2025 IEEE International Conference on Data Mining Workshops (ICDMW) · doi:10.1109/icdmw69685.2025.00368
“Li et al. [9] have proposed a multimodal approach for cardiovascular disease (CVD) prediction by combining electrocardiogram (ECG) and phonocardiogram (PCG) signals.”
Multimodal Ensemble Deep Learning for Skin Cancer Detection
Madhuri, G., Jabbar, M. A., & Vijaylaxmi (2025) · International Conference on Intelligent Communication Networks and Computational Techniques (ICICNCT) · doi:10.1109/icicnct66124.2025.11232584
“Predicting cardiovascular disease has demonstrated promise, especially when clinical and imaging characteristics are combined [19].”
Cardiovascular Disease Prediction using AI And Streamlit Framework
G., S., S, R., E, N., S, N., Niveha Shree, A., & V, N. (2025) · 2025 5th International Conference on Soft Computing for Security Applications (ICSCSA) · doi:10.1109/icscsa66339.2025.11171262
“These images contain subtle cues that can be crucial for detecting early signs of heart disease, but they are usually overlooked in standard prediction setups [8].”
Fine-Tuning of Wav2Vec 2.0 for Multimodal Classification of Abnormal Heart Sounds
Marocchi, M., Fynn, M., & Rong, Y. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc58623.2025.11251626
Du, X., He, J., Zhang, C.-L., & Zang, Y. (2025) · Proceedings of the 4th International Conference on Biomedical and Intelligent Systems · doi:10.1145/3745034.3745131
GrpMean: predicting cardiovascular disease at an early stage
Bhagat, H. V., Sharma, S., Uppari, P., & Reddy, M. S. L. (2025) · Iran Journal of Computer Science · doi:10.1007/s42044-025-00262-8
Kumar, S. (2025) · Advances in Nonlinear Variational Inequalities · doi:10.52783/anvi.v28.3658
“Subsequent work, e.g., by Li et al. (2021) and Bharti et al. (2021), has extended such approaches with hybrid combinations of tradition and deep learning methods, evidencing a main interest in metric performance, such as accuracy, sensitivity, specificity and area under the ROC curve in the clinic.”
HEART ATTACK DETECTION BY HEART RATE MONITORING USING IOT TECHNIQUES
Tiwari, M., & Waoo, D. A. A. (2024) · ShodhKosh Journal of Visual and Performing Arts · doi:10.29121/shodhkosh.v5.i1.2024.1875
Multi-Modal Feature Integration in Machine Learning Predictions for Cardiovascular Diseases
G, N., & Balivada, S. (2023) · International Journal of Health Technology and Innovation · doi:10.60142/ijhti.v2i03.03
Classification of Cardiovascular Disease Using 2D-Image Representations of Phonocardiogram Signals
Ali, S., Ullah, M. O., & Mir, J. (2022) · 2022 International Conference on Emerging Technologies in Electronics, Computing and Communication (ICETECC) · doi:10.1109/icetecc56662.2022.10069108
“The first step in pre-processing involves resampling acquired PCG signal data to 2kHz, reducing complexity and preserving low and high-frequency components [1], [2].”
BackgroundMethodInfluentialChen, F., Cui, J.-G., Wang, R.-X., Shi, P., Wang, G., Wang, X., & Zhang, G. (2025) · MATEC Web of Conferences · doi:10.1051/matecconf/202541301007
- Authors
- Li, P., Hu, Y., & Liu, Z.-P.
- Affiliations
- Shandong University (CN)
- Corresponding author
- Hu, Yongmei
- Liu, Zhi-Ping
- Venue
- Biomedical Signal Processing and Control
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 66
- Pages
- 102474
- ISSN
- 1746-8094 (ISSN-L)
- OpenAlex
- W3132952621
- Cited by
- 116
- References
- 37
- Citations by year
- 2026: 19 · 2025: 42 · 2024: 22 · 2023: 18 · 2022: 8 · 2021: 7
- FWCI
- 9.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Biomedical Signal Processing and Control (publishedVersion)
- Funders
- Shandong University
- Key Technology Research and Development Program of Shandong (2019JZZY010423)
- National Natural Science Foundation of China (61572287, 61973190)
- Ministry of Science and Technology of the People's Republic of China (2018IM020200)
- Natural Science Foundation of Shandong Province (ZR2020ZD25)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Keywords
- cardiovascular disease prediction, electrocardiogram, phonocardiogram, multimodal fusion, deep feature extraction, genetic algorithm feature selection, support vector machine classification, cardiovascular disease diagnosis
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