Varada Rajkumar Kukkala¹, Surapaneni Phani Praveen², Naga Satya Koti Mani Kumar Tirumanadham³, Parvathaneni Naga Srinivasu^4,5,*

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1085-1112, 2024, DOI:10.32604/csse.2024.053603 - 13 September 2024

Abstract This paper investigates the application of machine learning to develop a response model to cardiovascular problems and the use of AdaBoost which incorporates an application of Outlier Detection methodologies namely; Z-Score incorporated with Grey Wolf Optimization (GWO) as well as Interquartile Range (IQR) coupled with Ant Colony Optimization (ACO). Using a performance index, it is shown that when compared with the Z-Score and GWO with AdaBoost, the IQR and ACO, with AdaBoost are not very accurate (89.0% vs. 86.0%) and less discriminative (Area Under the Curve (AUC) score of 93.0% vs. 91.0%). The Z-Score and GWO… More >

Deema Mohammed Alsekait¹, Ahmed Younes Shdefat², Ayman Nabil³, Asif Nawaz^4,*, Muhammad Rizwan Rashid Rana⁴, Zohair Ahmed⁵, Hanaa Fathi⁶, Diaa Salama AbdElminaam^6,7,8

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 >

Wenjie Dong^1,2,#, Zhibin Hong^1,#, Aqian Wang², Kaiyu Jiang², Hai Zhu², Fu zhang², Zhaoxia Guo², Hongling Su^2,*, Yunshan Cao^3,*

Congenital Heart Disease, Vol.19, No.3, pp. 325-339, 2024, DOI:10.32604/chd.2024.052267 - 26 July 2024

Abstract Background: Current guidelines for managing pulmonary arterial hypertension (PAH) recommend a risk stratification approach. However, the applicability and accuracy of these strategies for PAH associated with congenital heart disease (PAH-CHD) require further validation. This study aims to validate the reliability and predictive accuracy of a simplified stratification strategy for PAH-CHD patients over a three-year follow-up. Additionally, new prognostic variables are identified and novel risk stratification methods are developed for assessing and managing PAH-CHD patients. Methods: This retrospective study included 126 PAH-CHD patients. Clinical and biochemical variables across risk groups were assessed using Kruskal-Wallis and Fisher’s… More > Graphic Abstract

Jiawang Xiao, Jianming Wang, Zhongchao Wang, Lili Meng, Ming Zhao, Qiguang Wang^*

Congenital Heart Disease, Vol.19, No.3, pp. 293-303, 2024, DOI:10.32604/chd.2024.051427 - 26 July 2024

Abstract Background: Transcatheter closure (TCC) has emerged as the preferred treatment for selected congenital heart disease (CHD). While TCC offers benefits for patients with postoperative residual shunts, understanding its mid- and long-term efficacy and safety remains crucial. Objective: This study aims to assess the mid- and long-term safety and efficacy of TCC for patients with residual atrial or ventricular septal shunts following CHD correction. Methods: In this consecutive retrospective study, we enrolled 35 patients with residual shunt who underwent TCC or surgical repair of CHD between June 2011 to October 2022. TCC candidacy was determined based on… More >

Batyrkhan Omarov^1,2,*, Meirzhan Baikuvekov¹, Daniyar Sultan¹, Nurzhan Mukazhanov³, Madina Suleimenova², Maigul Zhekambayeva³

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 341-359, 2024, DOI:10.32604/cmc.2024.052437 - 18 July 2024

Abstract This research introduces an innovative ensemble approach, combining Deep Residual Networks (ResNets) and Bidirectional Gated Recurrent Units (BiGRU), augmented with an Attention Mechanism, for the classification of heart arrhythmias. The escalating prevalence of cardiovascular diseases necessitates advanced diagnostic tools to enhance accuracy and efficiency. The model leverages the deep hierarchical feature extraction capabilities of ResNets, which are adept at identifying intricate patterns within electrocardiogram (ECG) data, while BiGRU layers capture the temporal dynamics essential for understanding the sequential nature of ECG signals. The integration of an Attention Mechanism refines the model’s focus on critical segments… More >

Adil Hussain^1,*, 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 >

Shuliang Xia^1,2,3,#, Huikang Tao^2,#, Shixin Su⁴, Xinxin Chen², Li Ma², Jianru Li⁵, Bei Gao⁶, Xumei Liu⁵, Lei Pi⁷, Jinqing Feng⁴, Fengxiang Li², Jia Li^4,*, Zhiwei Zhang^1,3,*

Congenital Heart Disease, Vol.19, No.2, pp. 247-256, 2024, DOI:10.32604/chd.2024.052583 - 16 May 2024

Abstract Aims: Multiple genes and environmental factors are known to be involved in congenital heart disease (CHD), but epigenetic variation has received little attention. Monozygotic (MZ) twins with CHD provide a unique model for exploring this phenomenon. In order to investigate the potential role of Deoxyribonucleic Acid (DNA) methylation in CHD pathogenesis, the present study examined DNA methylation variation in MZ twins discordant for CHD, especially ventricular septal defect (VSD). Methods and Results: Using genome-wide DNA methylation profiles, we identified 4004 differentially methylated regions (DMRs) in 18 MZ twin pairs discordant for CHD, and 2826 genes were… More > Graphic Abstract

Dominique Vervoort^1,2,3,*, Amy Verstappen³, Sreehari Madhavankutty Nair⁴, Chong Chin Eu⁵, Bistra Zheleva^3,6

Congenital Heart Disease, Vol.19, No.2, pp. 131-138, 2024, DOI:10.32604/chd.2024.049814 - 16 May 2024

Abstract This article has no abstract. More >

Camden L. Hebson^1,*, Kyle Bliton², Amr Y. Hammouda¹, Kaitlyn Barr³, W. Hampton Gray⁴, Mohini Gunnett², Waldemar F. Carlo¹

Congenital Heart Disease, Vol.19, No.2, pp. 185-195, 2024, DOI:10.32604/chd.2024.048871 - 16 May 2024

Abstract D-transposition of the great arteries (d-TGA) is surgically repaired with the arterial switch operation (ASO) with excellent results, however short and long-term morbidities still develop including neurocognitive delay. Clinically significant central sleep apnea is uncommon in non-premature infants, but when present indicates immature autonomic control of respiration likely due to a neurologic disorder. We report the unanticipated finding of central sleep apnea in four-term neonates with d-TGA after uncomplicated ASO, with the short-term complication of delayed hospital discharge and long-term concerns regarding this early marker of brain immaturity and its hindrance to normal development. Within More >

Yang Wang^#, Xun Yang^#, Mingtang Ye, Yuhang Zhao, Runsen Chen, Min Da, Zhiqi Wang, Xuming Mo, Jirong Qi^*

Congenital Heart Disease, Vol.19, No.2, pp. 219-231, 2024, DOI:10.32604/chd.2024.048314 - 16 May 2024

Abstract Congenital heart disease (CHD), the most prevalent congenital ailment, has seen advancements in the “dual indicator” screening program. This facilitates the early-stage diagnosis and treatment of children with CHD, subsequently enhancing their survival rates. While cardiac auscultation offers an objective reflection of cardiac abnormalities and function, its evaluation is significantly influenced by personal experience and external factors, rendering it susceptible to misdiagnosis and omission. In recent years, continuous progress in artificial intelligence (AI) has enabled the digital acquisition, storage, and analysis of heart sound signals, paving the way for intelligent CHD auscultation-assisted diagnostic technology. Although More > Graphic Abstract