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  • Open Access


    Artificial Intelligence Enabled Decision Support System on E-Healthcare Environment

    B. Karthikeyan1,*, K. Nithya2, Ahmed Alkhayyat3, Yousif Kerrar Yousif4

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2299-2313, 2023, DOI:10.32604/iasc.2023.032585

    Abstract In today’s digital era, e-healthcare systems exploit digital technologies and telecommunication devices such as mobile devices, computers and the internet to provide high-quality healthcare services. E-healthcare decision support systems have been developed to optimize the healthcare services and enhance a patient’s health. These systems enable rapid access to the specialized healthcare services via reliable information, retrieved from the cases or the patient histories. This phenomenon reduces the time taken by the patients to physically visit the healthcare institutions. In the current research work, a new Shuffled Frog Leap Optimizer with Deep Learning-based Decision Support System… More >

  • Open Access


    Deep Learning Enabled Intelligent Healthcare Management System in Smart Cities Environment

    Hanan Abdullah Mengash1, Lubna A. Alharbi2, Saud S. Alotaibi3, Sarab AlMuhaideb4, Nadhem Nemri5, Mrim M. Alnfiai6, Radwa Marzouk1, Ahmed S. Salama7, Mesfer Al Duhayyim8,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4483-4500, 2023, DOI:10.32604/cmc.2023.032588

    Abstract In recent times, cities are getting smart and can be managed effectively through diverse architectures and services. Smart cities have the ability to support smart medical systems that can infiltrate distinct events (i.e., smart hospitals, smart homes, and community health centres) and scenarios (e.g., rehabilitation, abnormal behavior monitoring, clinical decision-making, disease prevention and diagnosis postmarking surveillance and prescription recommendation). The integration of Artificial Intelligence (AI) with recent technologies, for instance medical screening gadgets, are significant enough to deliver maximum performance and improved management services to handle chronic diseases. With latest developments in digital data collection,… More >

  • Open Access


    An Intelligent Cardiovascular Diseases Prediction System Focused on Privacy

    Manjur Kolhar*, Mohammed Misfer

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 529-542, 2023, DOI:10.32604/iasc.2023.030098

    Abstract Machine learning (ML) and cloud computing have now evolved to the point where they are able to be used effectively. Further improvement, however, is required when both of these technologies are combined to reap maximum benefits. A way of improving the system is by enabling healthcare workers to select appropriate machine learning algorithms for prediction and, secondly, by preserving the privacy of patient data so that it cannot be misused. The purpose of this paper is to combine these promising technologies to maintain the privacy of patient data during the disease prediction process. Treatment of… More >

  • Open Access


    A Ring-Reinforced Right Ventricle to Pulmonary Artery Conduit is Associated with Better Regional Mechanics after Stage I Norwood Operation

    Benjamin Zielonka1,2,*, David M. Harrild1,2, Sunil J. Ghelani1,2, Eleni G. Elia1,2, Christopher W. Baird3,4, Andrew J. Powell1,2, Rahul H. Rathod1,2

    Congenital Heart Disease, Vol.17, No.5, pp. 591-603, 2022, DOI:10.32604/chd.2022.021509

    Abstract Background: The right ventricle to pulmonary artery conduit (RVPAC) may impair right ventricular (RV) function in patients with functional single right ventricles. Modification of the RVPAC using a ring-reinforced end with dunked insertion into the RV through a limited ventriculotomy may reduce the impact on RV function. We compared RV segmental strain between patients with a traditional RVPAC and ring-reinforced RVPAC using feature tracking cardiovascular magnetic resonance (CMR) imaging. Methods: Patients with CMR examinations after Stage I operation with RVPAC between 2000 and 2018 were reviewed. Ventricular mass, volumes, late gadolinium enhancement (LGE), and peak radial and… More >

  • Open Access


    DLMNN Based Heart Disease Prediction with PD-SS Optimization Algorithm

    S. Raghavendra1, Vasudev Parvati2, R. Manjula3, Ashok Kumar Nanda4, Ruby Singh5, D. Lakshmi6, S. Velmurugan7,*

    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… More >

  • Open Access


    Efficacy, Safety and Characteristics of the Amplatzer Vascular Plug II and IV Utilization for Various Percutaneous Occlusions in Children under 10 Years

    Hugues Lucron1,*, Alban-Elouen Baruteau2,3, Caroline Ovaert4, Ali Houeijeh5, Mélanie Brard1, Patrice Guerin2, François Bourlon6, Claire Dauphin7, Saskia Tuttle1, Maha Tagorti3, Rishika Banydeen8, François Godart5

    Congenital Heart Disease, Vol.17, No.4, pp. 421-436, 2022, DOI:10.32604/chd.2022.020835

    Abstract Objectives: We aim to describe the efficacy, safety, and characteristics of the Amplatzer Vascular Plug (AVP) II and IV “off-label” use for multiple cardiovascular occlusions in children under 10 years. Methods: Observational retrospective multicenter (2007–2020, 6 centers) review of paediatric procedures using AVP II or IV. Results: A total of 125 children (49.6% aged ≤ 1 year, 147 lesions) underwent 136 successive procedures (success rate: 98.5%) using 169 devices (109 AVP IV, 60 AVP II). The mean device diameter was 7.7 ± 3.2 mm (4–20 mm). The median AVP size to vessel diameter ratio was 1.3 (0–2). The median… More > Graphic Abstract

    Efficacy, Safety and Characteristics of the Amplatzer Vascular Plug II and IV Utilization for Various Percutaneous Occlusions in Children under 10 Years

  • Open Access


    Pregnancy in Patients with Shone Complex: A Single-Center Case Series

    Rachel Gardner1, Emily Durbak1, Rachael Baird2, Katherine Singh2, Jeff Chapa2, David Majdalany3,*

    Congenital Heart Disease, Vol.17, No.2, pp. 147-160, 2022, DOI:10.32604/chd.2022.017366

    Abstract Background: There is limited literature written on the course and outcomes for pregnant mothers with Shone complex. Methods: We describe a case series of five pregnancies in four women with Shone complex within a multidisciplinary cardio-obstetrics clinic from 2016–2018. Results: Maternal age ranged from 21–39 years. Three patients had preserved left ventricular function while one had moderately decreased function. Gestational age at presentation ranged from 6–15 weeks. There were three successful pregnancies (mean gestational age = 37 weeks, range 35–39 weeks) with one patient accounting for two unsuccessful pregnancies. All infants were delivered via Cesarean section. One More >

  • Open Access


    Handling High Dimensionality in Ensemble Learning for Arrhythmia Prediction

    Fuad Ali Mohammed Al-Yarimi*

    Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1729-1742, 2022, DOI:10.32604/iasc.2022.022418

    Abstract Computer-aided arrhythmia prediction from ECG (electrocardiograms) is essential in clinical practices, which promises to reduce the mortality caused by inexperienced clinical practitioners. Moreover, computer-aided methods often succeed in the early detection of arrhythmia scope from electrocardiogram reports. Machine learning is the buzz of computer-aided clinical practices. Particularly, computer-aided arrhythmia prediction methods highly adopted machine learning methods. However, the high dimensionality in feature values considered for the machine learning models’ training phase often causes false alarming. This manuscript addressed the high dimensionality in the learning phase and proposed an (Ensemble Learning method for Arrhythmia Prediction) ELAP… More >

  • Open Access


    Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals

    S. Karthik1, M. Santhosh1,*, M. S. Kavitha1, A. Christopher Paul2

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 183-199, 2022, DOI:10.32604/csse.2022.021698

    Abstract Automated biomedical signal processing becomes an essential process to determine the indicators of diseased states. At the same time, latest developments of artificial intelligence (AI) techniques have the ability to manage and analyzing massive amounts of biomedical datasets results in clinical decisions and real time applications. They can be employed for medical imaging; however, the 1D biomedical signal recognition process is still needing to be improved. Electrocardiogram (ECG) is one of the widely used 1-dimensional biomedical signals, which is used to diagnose cardiovascular diseases. Computer assisted diagnostic models find it difficult to automatically classify the… More >

  • Open Access


    Heart Sound Analysis for Abnormality Detection

    Zainab Arshad1, Sohail Masood Bhatti2,*, Huma Tauseef3, Arfan Jaffar2

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1195-1205, 2022, DOI:10.32604/iasc.2022.022160

    Abstract According to the World Health Organization, 31% death rate in the World is because of cardiovascular diseases like heart arrhythmia and heart failure. Early diagnosis of heart problems may help in timely treatment of the patients and hence control death rate. Heart sounds are good signals of heart health if examined by an expert. Moreover, heart sounds can be analyzed with inexpensive and portable medical devices. Automatic heart sound classification can be very useful in diagnosing heart problems. Major focus of this research is to study the existing techniques for heart sound classification and develop More >

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