Ghada Abdulsalam¹, Souham Meshoul^2,*, Hadil Shaiba³

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 761-779, 2023, DOI:10.32604/iasc.2023.032262

Abstract Nowadays, quantum machine learning is attracting great interest in a wide range of fields due to its potential superior performance and capabilities. The massive increase in computational capacity and speed of quantum computers can lead to a quantum leap in the healthcare field. Heart disease seriously threatens human health since it is the leading cause of death worldwide. Quantum machine learning methods can propose effective solutions to predict heart disease and aid in early diagnosis. In this study, an ensemble machine learning model based on quantum machine learning classifiers is proposed to predict the risk of heart disease. The proposed… More >

Jodi L. Feinberg¹, Peter Sheng², Stephanie Pena², Adam J. Small¹, Susanna Wendelboe¹, Katlyn Nemani³, Vikram Agrawal⁴, Dan G. Halpern^1,*

Congenital Heart Disease, Vol.17, No.5, pp. 519-531, 2022, DOI:10.32604/chd.2022.024174

Abstract Background: Adults with congenital heart disease (ACHD) have increased prevalence of mood and anxiety disorders. There are limited data regarding the influence of the COVID-19 pandemic on the mental health and health behaviors of these patients. Objective: The purpose is to evaluate the perceptions, emotions, and health behaviors of ACHD patients during the COVID-19 pandemic. Methods: In this cross-sectional study of ACHD patients, we administered surveys evaluating self-reported emotions, perceptions and health behaviors. Logistic regressions were performed to determine the adjusted odds of displaying each perception, emotion and health behavior based on predictor variables. Results: Ninety-seven patients (mean age 38.3… More > Graphic Abstract

Jae Hee Seol^1,4,#, Se Yong Jung^1,#, Jae Young Choi¹, Han Ki Park², Young Hwan Park², Nam Kyun Kim^1,3,*

Congenital Heart Disease, Vol.17, No.5, pp. 569-578, 2022, DOI:10.32604/chd.2022.018436

Abstract Objective: Junctional ectopic tachycardia is common after cardiac surgery for congenital heart disease. However, its incidence and related risk factors in infants after cardiac surgery are not well known. The objective of this study was to determine the overall incidence and related risk factors for junctional ectopic tachycardia in neonates and infants. Methods: We enrolled a total of 271 patients aged <1 year who underwent open cardiac surgery at Severance Cardiovascular Hospital from January 2018 to December 2020. Exclusion criteria were immediate postoperative mortality, other arrhythmias detected in the perioperative period, and prematurity. Result: The overall incidence of junctional ectopic… More >

Nisha Mary¹, Bilal Khan¹, Abdullah A. Asiri², Fazal Muhammad^3,*, Salman Khan³, Samar Alqhtani⁴, Khlood M. Mehdar⁵, Hanan Talal Halwani⁴, Muhammad Irfan⁶, Khalaf A. Alshamrani²

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6595-6616, 2022, DOI:10.32604/cmc.2022.032384

Abstract Heart disease prognosis (HDP) is a difficult undertaking that requires knowledge and expertise to predict early on. Heart failure is on the rise as a result of today’s lifestyle. The healthcare business generates a vast volume of patient records, which are challenging to manage manually. When it comes to data mining and machine learning, having a huge volume of data is crucial for getting meaningful information. Several methods for predicting HD have been used by researchers over the last few decades, but the fundamental concern remains the uncertainty factor in the output data, as well as the need to decrease… More >

Abeer Abdulaziz AlArfaj, Hanan Ahmed Hosni Mahmoud^*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2527-2540, 2023, DOI:10.32604/iasc.2023.030168

Abstract The detection of heart disease is a problematic task in medical research. This diagnosis utilizes a thorough analysis of the clinical tests from the patient’s medical history. The massive advances in deep learning models pursue the development of intelligent computerized systems that aid medical professionals to detect the disease type with the internet of things support. Therefore, in this paper, we propose a deep learning model for elderly patients to aid and enhance the diagnosis of heart disease. The proposed model utilizes a deeper neural architecture with multiple perceptron layers with regularization learning techniques. The model performance is verified with… More >

S. Raghavendra¹, Vasudev Parvati², R. Manjula³, Ashok Kumar Nanda⁴, Ruby Singh⁵, D. Lakshmi⁶, S. Velmurugan^7,*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1353-1368, 2023, DOI:10.32604/iasc.2023.027977

Abstract In contemporary medicine, cardiovascular disease is a major public health concern. Cardiovascular diseases are one of the leading causes of death worldwide. They are classified as vascular, ischemic, or hypertensive. Clinical information contained in patients’ Electronic Health Records (EHR) enables clinicians to identify and monitor heart illness. Heart failure rates have risen dramatically in recent years as a result of changes in modern lifestyles. Heart diseases are becoming more prevalent in today’s medical setting. Each year, a substantial number of people die as a result of cardiac pain. The primary cause of these deaths is the improper use of pharmaceuticals… More >

Eszter Vojcek^1,2,*, V. Anna Gyarmathy^3,4, Rozsa Graf⁵, Anna M. Laszlo⁶, Laszlo Ablonczy⁷, Zsolt Prodan⁷, Istvan Seri^1,8

Congenital Heart Disease, Vol.17, No.4, pp. 447-461, 2022, DOI:10.32604/chd.2022.022274

Abstract Objective: Neonates with congenital heart disease (CHD) and perinatal stroke have high mortality and survivors are at risk for poor long-term neurodevelopmental outcome. The aim of this study was to assess the risk factors and outcome of neonates with both CHD and MRI-confirmed perinatal stroke (Study Group) and compare those to the risk factors and outcome of infants matched for CHD without stroke (Control-1) and of infants matched for MRI-confirmed stroke without CHD (Control-2). Methods: We conducted a population-based case-control study enrolling 28 term neonates with CHD and MRI-confirmed acute perinatal stroke born between 2007–2017 in the Central-Hungarian Region. Each… More > Graphic Abstract

Tong Pang^#, Li Jiang^#, Yi Zhang, Mengxi Yang, Jin Wang, Yuan Li^*, Zhigang Yang^*

Congenital Heart Disease, Vol.17, No.4, pp. 479-489, 2022, DOI:10.32604/chd.2022.020401

Abstract Background: To evaluate the qualitative and quantitative differences between intracardiac and extracardiac vascular malformations in patients with a single atrium (SA), single ventricle (SV) and single atrium-single ventricle (SA-SV) using dual-source CT (DSCT), and to compare the diagnostic performances of DSCT and transthoracic echocardiography (TTE). Methods: This retrospective study included 24 SA, 75 SV and 24 SA-SV patients who underwent both DSCT and TTE before surgery. The diagnostic values of DSCT and TTE for intracardiac and extracardiac malformations were compared according to the surgical results. The diameters of the major artery and vein were measured and calculated based on DSCT… More >

Fatma A. Taha^1,2,*, Osama Amoudi¹, Fareed Alnozha¹, Reda Abuelatta¹

Congenital Heart Disease, Vol.17, No.4, pp. 399-419, 2022, DOI:10.32604/chd.2022.020174

Abstract Background: Adult patients with congenital heart disease (ACHD) might be at high risk of Coronavirus disease- 2019 (COVID-19). This study aimed to report on a one-year tertiary center experience regards COVID-19 infection in ACHD patients. Methods: This is a one-year (March-2020 to March-2021) tertiary-center retrospective study that enrolled all ACHD patients; COVID-19 positive patients’ medical records, and management were reported. Results: We recorded 542 patients, 205 (37.8%) COVID-19-positive, and 337 (62.2%) COVID-19-negative patients. Palliated single ventricle and Eisenmenger syndrome patients were more vulnerable to COVID-19 infection (P < 0.05*). Cardiovascular COVID-19 complications were arrhythmias in 47 (22.9%) patients, heart failure… More >

Nisha Mary¹, Bilal Khan^1,*, Abdullah A Asiri², Fazal Muhammad³, Samar Alqhtani⁴, Khlood M Mehdar⁵, Hanan Talal Halwani⁴, Turki Aleyani⁴, Khalaf A Alshamrani²

Journal of Intelligent Medicine and Healthcare, Vol.1, No.1, pp. 11-31, 2022, DOI:10.32604/jimh.2022.030161

Abstract Prognosis of HD is a complex task that requires experience and expertise to predict in the early stage. Nowadays, heart failure is rising due to the inherent lifestyle. The healthcare industry generates dense records of patients, which cannot be managed manually. Such an amount of data is very significant in the field of data mining and machine learning when gathering valuable knowledge. During the last few decades, researchers have used different approaches for the prediction of HD, but still, the major problem is the uncertainty factor in the output data and also there is a need to reduce the error… More >