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  • Open Access

    ARTICLE

    Predicting Factors of Physical Activity among Children with Congenital Heart Disease after Corrective Surgery

    Nuttanicha Sriboonyawattana*, Thitima Suklerttrakul

    Congenital Heart Disease, Vol.20, No.2, pp. 231-244, 2025, DOI:10.32604/chd.2025.064662 - 30 April 2025

    Abstract Objective: Many children with fully corrected congenital heart disease (CHD) avoid physical activity (PA). This descriptive study sought to determine child and parental factors that could predict PA levels in Thai children after corrective surgery. Methods: Ninety school-aged children with fully corrected CHD were recruited from a cardiology clinic at a university hospital in northern Thailand. Data collection involved five validated questionnaires: (1) the Modified Thai Adolescent’s Physical Activity Questionnaire, (2) the Child Health Status Questionnaire-Forms I and II, (3) the Parental Knowledge on School-aged Children’s Physical Activity Scale, (4) the Perceived Self-efficacy to Physical Activity… More >

  • Open Access

    REVIEW

    Variants and Molecular Mechanism of NOTCH1 in Congenital Heart Disease

    Hongqun Xiang1, Jian Zhuang2,3, Luoning Bao4,5, Yan Shi2,3,*

    Congenital Heart Disease, Vol.20, No.2, pp. 245-263, 2025, DOI:10.32604/chd.2025.064366 - 30 April 2025

    Abstract Congenital heart disease (CHD) is the most common birth defect, with 34% of cases attributed to genetic variants. NOTCH1, a multi-domain transmembrane protein, regulates heart development by controlling the differentiation and migration of myocardial mesoderm cells, and different variants are present in different types of CHD. In this review, we aim to provide a detailed description of NOTCH1 structural domains and their functions, highlighting NOTCH1 variants in CHD and the molecular mechanisms through which they contribute to CHD occurrence. NOTCH1 has two main domains, the NOTCH extracellular domain (NECD) and the NOTCH intracellular domain… More >

  • Open Access

    ARTICLE

    Hemodynamic Profile Based on Right Heart Catheterization in Adult Acyanotic Congenital Heart Disease with Pulmonary Hypertension

    Dina Anggraini1, Kurnia Wahyudi2, Melawati Hasan3, Sri Endah Rahayuningsih4,*, Charlotte Johanna Cool3

    Congenital Heart Disease, Vol.20, No.2, pp. 133-141, 2025, DOI:10.32604/chd.2025.064164 - 30 April 2025

    Abstract Background: Congenital heart disease (CHD) occurs in 9 out of 100 births and is the leading cause of birth defects, with acyanotic CHD being more common. The incidence of adult CHD is rising faster than pediatric CHD. Pulmonary hypertension is the most common complication in untreated CHD patients. Methods: This study is retrospective descriptive research based on medical record data and the results of right heart catheterization examinations in adult acyanotic CHD aged ≥18 years and free from other organ disorders. Results: A total of 103 patients met the inclusion criteria, the majority were young… More >

  • Open Access

    ARTICLE

    A Transformer Based on Feedback Attention Mechanism for Diagnosis of Coronary Heart Disease Using Echocardiographic Images

    Chunlai Du1,#, Xin Gu1,#, Yanhui Guo2,*, Siqi Guo3, Ziwei Pang3, Yi Du3, Guoqing Du3,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3435-3450, 2025, DOI:10.32604/cmc.2025.060212 - 16 April 2025

    Abstract Coronary artery disease is a highly lethal cardiovascular condition, making early diagnosis crucial for patients. Echocardiograph is employed to identify coronary heart disease (CHD). However, due to issues such as fuzzy object boundaries, complex tissue structures, and motion artifacts in ultrasound images, it is challenging to detect CHD accurately. This paper proposes an improved Transformer model based on the Feedback Self-Attention Mechanism (FSAM) for classification of ultrasound images. The model enhances attention weights, making it easier to capture complex features. Experimental results show that the proposed method achieves high levels of accuracy, recall, precision, F1 More >

  • Open Access

    ARTICLE

    Heart Disease Prediction Model Using Feature Selection and Ensemble Deep Learning with Optimized Weight

    Iman S. Al-Mahdi1, Saad M. Darwish1,*, Magda M. Madbouly2

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 875-909, 2025, DOI:10.32604/cmes.2025.061623 - 11 April 2025

    Abstract Heart disease prediction is a critical issue in healthcare, where accurate early diagnosis can save lives and reduce healthcare costs. The problem is inherently complex due to the high dimensionality of medical data, irrelevant or redundant features, and the variability in risk factors such as age, lifestyle, and medical history. These challenges often lead to inefficient and less accurate models. Traditional prediction methodologies face limitations in effectively handling large feature sets and optimizing classification performance, which can result in overfitting poor generalization, and high computational cost. This work proposes a novel classification model for heart… More >

  • Open Access

    ARTICLE

    Generating Synthetic Data for Machine Learning Models from the Pediatric Heart Network Fontan I Dataset

    Vatche Bahudian, John Valdovinos*

    Congenital Heart Disease, Vol.20, No.1, pp. 115-127, 2025, DOI:10.32604/chd.2025.063991 - 18 March 2025

    Abstract Background: The population of Fontan patients, patients born with a single functioning ventricle, is growing. There is a growing need to develop algorithms for this population that can predict health outcomes. Artificial intelligence models predicting short-term and long-term health outcomes for patients with the Fontan circulation are needed. Generative adversarial networks (GANs) provide a solution for generating realistic and useful synthetic data that can be used to train such models. Methods: Despite their promise, GANs have not been widely adopted in the congenital heart disease research community due, in some part, to a lack of knowledge… More >

  • Open Access

    REVIEW

    Maternal Diabetes Mellitus and Congenital Heart Diseases: Systematic Review

    Roberto Noya Galluzzo1, Karine Souza Da Correggio1, Aldo von Wangenheim2, Heron Werner3, Nathalie Jeanne Bravo-Valenzuela4, Edward Araujo Júnior5,6,*, Alexandre Sherlley Casimiro Onofre7

    Congenital Heart Disease, Vol.20, No.1, pp. 89-101, 2025, DOI:10.32604/chd.2025.063014 - 18 March 2025

    Abstract Introduction: Diabetes mellitus (DM), a metabolic disorder, leads to organ damage due to chronic hyperglycemia with multiple pathogenic processes. Gestational diabetes mellitus (GDM) poses risks to mothers and offspring, increasing the incidence of structural congenital heart disease (CHD) and myocardial hypertrophy in newborns. Objective: This review aimed to examine the association between maternal diabetes mellitus and CHD. Methods: This systematic review used the STROBE and TRIPOD checklists registered in PROSPERO (CRD42024513858). It focused on diagnostic test accuracy using the Munn et al. protocol for systematic assessment, emphasizing the “PIRD”: Population, Index Test, Reference Test, Diagnosis of Interest.… More >

  • Open Access

    REVIEW

    Challenges in the Transition and Transfer of Young Adults with Congenital Heart Disease in Latin America and the Caribbean: The “Timeliness Principle”

    John J. Araujo1,2,*

    Congenital Heart Disease, Vol.20, No.1, pp. 61-75, 2025, DOI:10.32604/chd.2025.062927 - 18 March 2025

    Abstract Today, more than 90% of children who are born with congenital heart disease survive and reach adulthood, especially in developed countries. Consequently, the population of adults with congenital heart disease has increased significantly over the last few decades. In Latin America and the Caribbean countries, this same scenario is occurring at an accelerated pace. Loss to follow-up is a global problem in adults with congenital heart disease, ranging from 30–60%. In Latin America and Caribbean countries, it is estimated that less than 10% of adults with congenital heart disease are being followed. The small number More >

  • Open Access

    ARTICLE

    Harmonization of Heart Disease Dataset for Accurate Diagnosis: A Machine Learning Approach Enhanced by Feature Engineering

    Ruhul Amin1, Md. Jamil Khan1, Tonway Deb Nath1, Md. Shamim Reza2, Jungpil Shin3,*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3907-3919, 2025, DOI:10.32604/cmc.2025.061645 - 06 March 2025

    Abstract Heart disease includes a multiplicity of medical conditions that affect the structure, blood vessels, and general operation of the heart. Numerous researchers have made progress in correcting and predicting early heart disease, but more remains to be accomplished. The diagnostic accuracy of many current studies is inadequate due to the attempt to predict patients with heart disease using traditional approaches. By using data fusion from several regions of the country, we intend to increase the accuracy of heart disease prediction. A statistical approach that promotes insights triggered by feature interactions to reveal the intricate pattern… More >

  • Open Access

    ARTICLE

    Neural Network Algorithm Based on LVQ for Myocardial Infarction Detection and Localization Using Multi-Lead ECG Data

    Kassymbek Ozhikenov1, Zhadyra Alimbayeva1,*, Chingiz Alimbayev1,2,*, Aiman Ozhikenova1, Yeldos Altay1

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5257-5284, 2025, DOI:10.32604/cmc.2025.061508 - 06 March 2025

    Abstract Myocardial infarction (MI) is one of the leading causes of death globally among cardiovascular diseases, necessitating modern and accurate diagnostics for cardiac patient conditions. Among the available functional diagnostic methods, electrocardiography (ECG) is particularly well-known for its ability to detect MI. However, confirming its accuracy—particularly in identifying the localization of myocardial damage—often presents challenges in practice. This study, therefore, proposes a new approach based on machine learning models for the analysis of 12-lead ECG data to accurately identify the localization of MI. In particular, the learning vector quantization (LVQ) algorithm was applied, considering the contribution… More >

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