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  • Open Access

    ARTICLE

    Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

    Cheng Wang1, Haoran Zhu2,*, Congjun Rao2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 691-718, 2024, DOI:10.32604/cmes.2023.029258

    Abstract Cardiovascular disease (CVD) has gradually become one of the main causes of harm to the life and health of residents. Exploring the influencing factors and risk assessment methods of CVD has become a general trend. In this paper, a machine learning-based decision-making mechanism for risk assessment of CVD is designed. In this mechanism, the logistics regression analysis method and factor analysis model are used to select age, obesity degree, blood pressure, blood fat, blood sugar, smoking status, drinking status, and exercise status as the main pathogenic factors of CVD, and an index system of risk assessment for CVD is established.… More >

  • Open Access

    ARTICLE

    Multi Head Deep Neural Network Prediction Methodology for High-Risk Cardiovascular Disease on Diabetes Mellitus

    B. Ramesh, Kuruva Lakshmanna*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2513-2528, 2023, DOI:10.32604/cmes.2023.028944

    Abstract Major chronic diseases such as Cardiovascular Disease (CVD), diabetes, and cancer impose a significant burden on people and healthcare systems around the globe. Recently, Deep Learning (DL) has shown great potential for the development of intelligent mobile Health (mHealth) interventions for chronic diseases that could revolutionize the delivery of health care anytime, anywhere. The aim of this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis, prognosis, management, and treatment of major chronic diseases and advance our understanding of the progress made in this rapidly developing field. Type 2… More > Graphic Abstract

    Multi Head Deep Neural Network Prediction Methodology for High-Risk Cardiovascular Disease on Diabetes Mellitus

  • Open Access

    ARTICLE

    Deep Learning Approach for Automatic Cardiovascular Disease Prediction Employing ECG Signals

    Muhammad Tayyeb1, Muhammad Umer1, Khaled Alnowaiser2, Saima Sadiq3, Ala’ Abdulmajid Eshmawi4, Rizwan Majeed5, Abdullah Mohamed6, Houbing Song7, Imran Ashraf8,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1677-1694, 2023, DOI:10.32604/cmes.2023.026535

    Abstract Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed lately. Currently, electrocardiogram (ECG) data is analyzed by medical experts to determine the cardiac abnormality, which is time-consuming. In addition, the diagnosis requires experienced medical experts and is error-prone. However, automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning architectures. This study proposes a simple multilayer perceptron (MLP) model for heart disease prediction to reduce computational complexity. ECG dataset containing averaged signals with window size 10 is… More >

  • Open Access

    REVIEW

    The role of periodontal disease in atherosclerotic cardiovascular disease

    XIWEI ZHAO1,#, JINSONG WANG1,2,#, YIFAN XU1, JIAN ZHOU5,*, LEI HU1,3,4,*

    BIOCELL, Vol.47, No.7, pp. 1431-1438, 2023, DOI:10.32604/biocell.2023.028217

    Abstract Atherosclerotic cardiovascular disease (ASCVD) includes a group of disorders of the heart and blood vessels and accounts for major morbidity and premature death worldwide. Periodontitis is a chronic inflammatory disease with the gradual destruction of supporting tissues around the teeth, including gingiva, periodontal ligament, alveolar bone, and cementum. Periodontitis has been found to potentially increase the risk of ASCVD. Generally, oral microorganisms and inflammation are the major factors for periodontitis to the incidence of ASCVD. Recently, evidence has shown that the loss of masticatory function is another important factor of periodontitis to the incidence of ASCVD. In this review, we… More > Graphic Abstract

    The role of periodontal disease in atherosclerotic cardiovascular disease

  • Open Access

    ARTICLE

    Optimized Three-Dimensional Cardiovascular Magnetic Resonance Whole Heart Imaging Utilizing Non-Selective Excitation and Compressed Sensing in Children and Adults with Congenital Heart Disease

    Ingo Paetsch1,*, Roman Gebauer2, Christian Paech2, Frank-Thomas Riede2, Sabrina Oebel1, Andreas Bollmann1, Christian Stehning3, Jouke Smink4, Ingo Daehnert2, Cosima Jahnke1

    Congenital Heart Disease, Vol.18, No.3, pp. 279-294, 2023, DOI:10.32604/chd.2023.029634

    Abstract Background: In congenital heart disease (CHD) patients, detailed three-dimensional anatomy depiction plays a pivotal role for diagnosis and therapeutical decision making. Hence, the present study investigated the applicability of an advanced cardiovascular magnetic resonance (CMR) whole heart imaging approach utilizing nonselective excitation and compressed sensing for anatomical assessment and interventional guidance of CHD patients in comparison to conventional dynamic CMR angiography. Methods: 86 consecutive pediatric patients and adults with congenital heart disease (age, 1 to 74 years; mean, 35 years) underwent CMR imaging including a free-breathing, ECG-triggered 3D nonselective SSFP whole heart acquisition using compressed SENSE (nsWHcs). Anatomical assessability and… More >

  • Open Access

    ARTICLE

    Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

    Wasif Akbar1, Adbul Mannan2, Qaisar Shaheen3,*, Mohammad Hijji4, Muhammad Anwar5, Muhammad Ayaz6

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6269-6286, 2023, DOI:10.32604/cmc.2023.036141

    Abstract Machine Learning (ML) has changed clinical diagnostic procedures drastically. Especially in Cardiovascular Diseases (CVD), the use of ML is indispensable to reducing human errors. Enormous studies focused on disease prediction but depending on multiple parameters, further investigations are required to upgrade the clinical procedures. Multi-layered implementation of ML also called Deep Learning (DL) has unfolded new horizons in the field of clinical diagnostics. DL formulates reliable accuracy with big datasets but the reverse is the case with small datasets. This paper proposed a novel method that deals with the issue of less data dimensionality. Inspired by the regression analysis, the… More >

  • Open Access

    ARTICLE

    A Semantic Adversarial Network for Detection and Classification of Myopic Maculopathy

    Qaisar Abbas1, Abdul Rauf Baig1,*, Ayyaz Hussain2

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1483-1499, 2023, DOI:10.32604/cmc.2023.036366

    Abstract The diagnosis of eye disease through deep learning (DL) technology is the latest trend in the field of artificial intelligence (AI). Especially in diagnosing pathologic myopia (PM) lesions, the implementation of DL is a difficult task because of the classification complexity and definition system of PM. However, it is possible to design an AI-based technique that can identify PM automatically and help doctors make relevant decisions. To achieve this objective, it is important to have adequate resources such as a high-quality PM image dataset and an expert team. The primary aim of this research is to design and train the… More >

  • Open Access

    ARTICLE

    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 (SFLODL-DSS) is designed for the… More >

  • Open Access

    ARTICLE

    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, AI techniques can be employed… More >

  • Open Access

    ARTICLE

    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 heart failure may be improved… More >

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