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  • Open Access

    ARTICLE

    Advanced ECG Signal Analysis for Cardiovascular Disease Diagnosis Using AVOA Optimized Ensembled Deep Transfer Learning Approaches

    Amrutanshu Panigrahi1, Abhilash Pati1, Bibhuprasad Sahu2, Ashis Kumar Pati3, Subrata Chowdhury4, Khursheed Aurangzeb5,*, Nadeem Javaid6, Sheraz Aslam7,*

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1633-1657, 2025, DOI:10.32604/cmc.2025.063562 - 09 June 2025

    Abstract The integration of IoT and Deep Learning (DL) has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management (PHM). Electrocardiograms (ECGs) are widely used for cardiovascular disease (CVD) diagnosis, but fluctuating signal patterns make classification challenging. Computer-assisted automated diagnostic tools that enhance ECG signal categorization using sophisticated algorithms and machine learning are helping healthcare practitioners manage greater patient populations. With this motivation, the study proposes a DL framework leveraging the PTB-XL ECG dataset to improve CVD diagnosis. Deep Transfer Learning (DTL) techniques extract features, followed by feature fusion to eliminate redundancy… More >

  • Open Access

    ARTICLE

    Multi-Label Machine Learning Classification of Cardiovascular Diseases

    Chih-Ta Yen1,*, Jung-Ren Wong2, Chia-Hsang Chang2

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 347-363, 2025, DOI:10.32604/cmc.2025.063389 - 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… More >

  • Open Access

    ARTICLE

    An AI-Enabled Framework for Transparency and Interpretability in Cardiovascular Disease Risk Prediction

    Isha Kiran1, Shahzad Ali2,3, Sajawal ur Rehman Khan4,5, Musaed Alhussein6, Sheraz Aslam7,8,*, Khursheed Aurangzeb6,*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5057-5078, 2025, DOI:10.32604/cmc.2025.058724 - 06 March 2025

    Abstract Cardiovascular disease (CVD) remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis, driven by risk factors such as hypertension, high cholesterol, and irregular pulse rates. Traditional diagnostic methods often struggle with the nuanced interplay of these risk factors, making early detection difficult. In this research, we propose a novel artificial intelligence-enabled (AI-enabled) framework for CVD risk prediction that integrates machine learning (ML) with eXplainable AI (XAI) to provide both high-accuracy predictions and transparent, interpretable insights. Compared to existing studies that typically focus on either optimizing ML… More >

  • Open Access

    ARTICLE

    Multi-Scale Dilated Convolution Network for SPECT-MPI Cardiovascular Disease Classification with Adaptive Denoising and Attenuation Correction

    A. Robert Singh1, Suganya Athisayamani2, Gyanendra Prasad Joshi3, Bhanu Shrestha4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 299-327, 2025, DOI:10.32604/cmes.2024.055599 - 17 December 2024

    Abstract Myocardial perfusion imaging (MPI), which uses single-photon emission computed tomography (SPECT), is a well-known estimating tool for medical diagnosis, employing the classification of images to show situations in coronary artery disease (CAD). The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks (CNNs). This paper uses a SPECT classification framework with three steps: 1) Image denoising, 2) Attenuation correction, and 3) Image classification. Image denoising is done by a U-Net architecture that ensures effective image denoising. Attenuation correction is implemented by a convolution neural network model that… More >

  • Open Access

    ARTICLE

    Optimizing outcomes in men with prostate cancer: the cardiovascular event lowering (CaELo) pathways

    E. David Crawford1, David Albala2, Marc B. Garnick3, Andrew W. Hahn4, Paul Maroni5, Rana R. McKay6, Martin Miner7, Peter Orio III8, Kshitij Pandit1, Scott Sellinger9, Evan Y. Yu10, Robert H. Eckel11

    Canadian Journal of Urology, Vol.31, No.2, pp. 11820-11825, 2024

    Abstract Introduction: Risk of cardiovascular disease is higher among men with prostate cancer than men without, and prostate cancer treatments (especially those that are hormonally based) are associated with increased cardiovascular risk.
    Materials and methods: An 11-member panel of urologic, medical, and radiation oncologists (along with a men’s health specialist and an endocrinologist/ preventive cardiologist) met to discuss current practices and challenges in the management of cardiovascular risk in prostate cancer patients who are taking androgen deprivation therapies (ADT) including LHRH analogues, alone and in combination with androgen-targeted therapies (ATTs).
    Results: The panel developed an assessment algorithm to categorize… More >

  • Open Access

    PROCEEDINGS

    Towards High-Fidelity and Efficient Computation for Diagnosis and Treatment of Cardiovascular Disease

    Lei Wang1,*, Blanca Rodriguez2, Xiaoyu Luo3, Charles Augarde4

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-2, 2024, DOI:10.32604/icces.2024.013350

    Abstract Cardiovascular disease is the leading cause of death worldwide. Disease-specific software, like FFRct from HeartFlow, and high-fidelity computational models within a general-purpose software, like Living Heart Project within Abaqus, are essential to revolutionise diagnosis and treatment of cardiovascular disease for clinicians and design of medical devices for industries. This talk presents our past researches on computational modelling of tear propagation in the aortic dissection [1-2] and of electromechanical coupling in the human heart with the finite element method [3], and our current exploration on high-fidelity and efficient computation and software development for diagnosis and treatment More >

  • Open Access

    REVIEW

    Advancement in CFD and Responsive AI to Examine Cardiovascular Pulsatile Flow in Arteries: A Review

    Priyambada Praharaj1,*, Chandrakant R. Sonawane2,*, Arunkumar Bongale2

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2021-2064, 2024, DOI:10.32604/cmes.2024.056289 - 31 October 2024

    Abstract This paper represents a detailed and systematic review of one of the most ongoing applications of computational fluid dynamics (CFD) in biomedical applications. Beyond its various engineering applications, CFD has started to establish a presence in the biomedical field. Cardiac abnormality, a familiar health issue, is an essential point of investigation by research analysts. Diagnostic modalities provide cardiovascular structural information but give insufficient information about the hemodynamics of blood. The study of hemodynamic parameters can be a potential measure for determining cardiovascular abnormalities. Numerous studies have explored the rheological behavior of blood experimentally and numerically.… More >

  • Open Access

    ARTICLE

    Heart-Net: A Multi-Modal Deep Learning Approach for Diagnosing Cardiovascular Diseases

    Deema Mohammed Alsekait1, Ahmed Younes Shdefat2, Ayman Nabil3, Asif Nawaz4,*, Muhammad Rizwan Rashid Rana4, Zohair Ahmed5, Hanaa Fathi6, Diaa Salama AbdElminaam6,7,8

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3967-3990, 2024, DOI:10.32604/cmc.2024.054591 - 12 September 2024

    Abstract Heart disease remains a leading cause of morbidity and mortality worldwide, highlighting the need for improved diagnostic methods. Traditional diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults, which can reduce accuracy, especially with poor-quality images. Additionally, these methods often require significant time and expertise, making them less accessible in resource-limited settings. Emerging technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision, ultimately improving patient outcomes and reducing healthcare costs. This study introduces Heart-Net, a multi-modal deep learning framework designed to… More >

  • Open Access

    ARTICLE

    Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter Optimization

    Tajim Md. Niamat Ullah Akhund1,2,*, Waleed M. Al-Nuwaiser3

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3485-3506, 2024, DOI:10.32604/cmc.2024.054222 - 12 September 2024

    Abstract This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST (Internet of Sensing Things) device. Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning. Significant improvements were observed across various models, with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score, recall, and precision. The study underscores the critical role of tailored hyperparameter tuning in optimizing these models, revealing diverse outcomes among algorithms. Decision Trees and Random Forests exhibited stable performance throughout the evaluation. While More >

  • Open Access

    ARTICLE

    Cardiovascular Disease Prediction Using Risk Factors: A Comparative Performance Analysis of Machine Learning Models

    Adil Hussain1,*, Ayesha Aslam2

    Journal on Artificial Intelligence, Vol.6, pp. 129-152, 2024, DOI:10.32604/jai.2024.050277 - 21 May 2024

    Abstract The diagnosis and prognosis of cardiovascular diseases are critical medical responsibilities that assist cardiologists in correctly classifying patients and treating them accordingly. The utilization of machine learning in the medical domain has witnessed a notable surge due to its ability to discern patterns from vast amounts of data. Machine learning algorithms that can categorize cases of cardiovascular illness may help doctors reduce the number of wrong diagnoses. This research investigates the efficacy of different machine learning algorithms in predicting cardiovascular disease in accordance with risk factors. This study utilizes a variety of machine learning models, More >

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