Sam Rahimzadeh Holagh¹, Bugao Xu^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2383-2399, 2025, DOI:10.32604/cmes.2025.072259 - 26 November 2025

Abstract Cardiovascular diseases (CVD) remain a leading cause of mortality worldwide, highlighting the need for precise risk assessment tools to support clinical decision-making. This study introduces a meta-learning model for predicting mortality risk in patients with CVD, classifying them into high-risk and low-risk groups. Data were collected from 868 patients at Tabriz Heart Hospital (THH) in Iran, along with two open-access datasets—the Cleveland Heart Disease (CHD) and Faisalabad Institute of Cardiology (FIC) datasets. Data preprocessing involved class balancing via the Synthetic Minority Over-Sampling Technique (SMOTE). Each dataset was then split into training and test sets, and… More >

Soumia Zertal^1,2,*, Asma Saighi^1,2, Sofia Kouah^1,2, Souham Meshoul^3,*, Zakaria Laboudi^2,4

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3737-3782, 2025, DOI:10.32604/cmes.2025.068558 - 30 September 2025

Abstract Cardiovascular diseases (CVDs) continue to present a leading cause of mortality worldwide, emphasizing the importance of early and accurate prediction. Electrocardiogram (ECG) signals, central to cardiac monitoring, have increasingly been integrated with Deep Learning (DL) for real-time prediction of CVDs. However, DL models are prone to performance degradation due to concept drift and to catastrophic forgetting. To address this issue, we propose a real-time CVDs prediction approach, referred to as ADWIN-GFR that combines Convolutional Neural Network (CNN) layers, for spatial feature extraction, with Gated Recurrent Units (GRU), for temporal modeling, alongside adaptive drift detection and… More > Graphic Abstract

Sami Alrabie^#,*, Ahmed Barnawi^#

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2481-2519, 2025, DOI:10.32604/cmes.2025.067977 - 31 August 2025

Abstract In the effort to enhance cardiovascular diagnostics, deep learning-based heart sound classification presents a promising solution. This research introduces a novel preprocessing method: iterative k-means clustering combined with silhouette score analysis, aimed at downsampling. This approach ensures optimal cluster formation and improves data quality for deep learning models. The process involves applying k-means clustering to the dataset, calculating the average silhouette score for each cluster, and selecting the cluster with the highest score. We evaluated this method using 10-fold cross-validation across various transfer learning models from different families and architectures. The evaluation was conducted on… More >

Kamepalli S. L. Prasanna¹, Vijaya J², Parvathaneni Naga Srinivasu¹, Babar Shah³, Farman Ali^4,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1603-1630, 2025, DOI:10.32604/cmc.2025.068707 - 29 August 2025

Abstract Cardiovascular disease prediction is a significant area of research in healthcare management systems (HMS). We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance. The existing heart disease detection systems using machine learning have not yet produced sufficient results due to the reliance on available data. We present Clustered Butterfly Optimization Techniques (RoughK-means+BOA) as a new hybrid method for predicting heart disease. This method comprises two phases: clustering data using Roughk-means (RKM) and data analysis using the butterfly optimization algorithm (BOA). The benchmark dataset from the UCI More >

Farhan Sarwar¹, Muhammad Shoaib Farooq¹, Nagwan Abdel Samee^2,*, Mona M. Jamjoom³, Imran Ashraf^4,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5897-5914, 2025, DOI:10.32604/cmc.2025.063832 - 30 July 2025

Abstract Environmental transition can potentially influence cardiovascular health. Investigating the relationship between such transition and heart disease has important applications. This study uses federated learning (FL) in this context and investigates the link between climate change and heart disease. The dataset containing environmental, meteorological, and health-related factors like blood sugar, cholesterol, maximum heart rate, fasting ECG, etc., is used with machine learning models to identify hidden patterns and relationships. Algorithms such as federated learning, XGBoost, random forest, support vector classifier, extra tree classifier, k-nearest neighbor, and logistic regression are used. A framework for diagnosing heart disease More >

Deepak Mahto¹, Sudhakar Kumar¹, Sunil K. Singh¹, Amit Chhabra¹, Irfan Ahmad Khan², Varsha Arya^3,4, Wadee Alhalabi⁵, Brij B. Gupta^6,7,8,9,*, Bassma Saleh Alsulami¹⁰

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3743-3767, 2025, DOI:10.32604/cmes.2025.063427 - 30 June 2025

Abstract Cardiovascular diseases (CVDs) remain one of the foremost causes of death globally; hence, the need for several must-have, advanced automated diagnostic solutions towards early detection and intervention. Traditional auscultation of cardiovascular sounds is heavily reliant on clinical expertise and subject to high variability. To counter this limitation, this study proposes an AI-driven classification system for cardiovascular sounds whereby deep learning techniques are engaged to automate the detection of an abnormal heartbeat. We employ FastAI vision-learner-based convolutional neural networks (CNNs) that include ResNet, DenseNet, VGG, ConvNeXt, SqueezeNet, and AlexNet to classify heart sound recordings. Instead of… More >

Siarhei A. Dabravolski^1,*, Michael I. Bukrinsky², Aleksandra S. Utkina³, Alessio L. Ravani⁴, Vasily N. Sukhorukov^5,6, Alexander N. Orekhov⁷

BIOCELL, Vol.49, No.6, pp. 931-959, 2025, DOI:10.32604/biocell.2025.062789 - 24 June 2025

Abstract Neutrophil extracellular traps (NET) have emerged as critical players in the pathogenesis of atherosclerosis and other cardiovascular diseases (CVD). These web-like structures, composed of DNA, histones, and granule proteins released by neutrophils, contribute significantly to both inflammation and thrombosis. This manuscript offers a comprehensive review of the recent literature on the involvement of NET in atherosclerosis, highlighting their interactions with various pathophysiological processes and their potential as biomarkers for CVD. Notably, the impact of radiation on NET formation is explored, emphasising how oxidative stress and inflammatory responses drive NET release, contributing to plaque instability. The… More >

Amrutanshu Panigrahi¹, Abhilash Pati¹, Bibhuprasad Sahu², Ashis Kumar Pati³, Subrata Chowdhury⁴, Khursheed Aurangzeb^5,*, Nadeem Javaid⁶, Sheraz Aslam^7,*

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 >

Chih-Ta Yen^1,*, 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 >

Isha Kiran¹, Shahzad Ali^2,3, Sajawal ur Rehman Khan^4,5, Musaed Alhussein⁶, Sheraz Aslam^7,8,*, Khursheed Aurangzeb^6,*

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 >