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  • Open Access


    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


    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


    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 >

  • 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 (ensemble learning-based arrhythmia prediction). The… More >

  • Open Access


    Heart Failure Patient Survival Analysis with Multi Kernel Support Vector Machine

    R. Sujatha1, Jyotir Moy Chatterjee2, NZ Jhanjhi3, Thamer A. Tabbakh4, Zahrah A. Almusaylim5,*

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 115-129, 2022, DOI:10.32604/iasc.2022.019133

    Abstract Heart failure (HF) is an intercontinental pandemic influencing in any event 26 million individuals globally and is expanding in commonness. HF healthiness consumptions are extensive and will increment significantly with a maturing populace. As per the World Health Organization (WHO), Cardiovascular diseases (CVDs) are the major reason for all-inclusive death, taking an expected 17.9 million lives per year. CVDs are a class of issues of the heart, blood vessels and include coronary heart sickness, cerebrovascular illness, rheumatic heart malady, and various other conditions. In the medical care industry, a lot of information is as often as possible created. Nonetheless, it… More >

  • Open Access


    Mechano-Sensing and shear stress-shielding by endothelial primary cilia: structure, composition, and function


    BIOCELL, Vol.45, No.5, pp. 1187-1199, 2021, DOI:10.32604/biocell.2021.016650

    Abstract Primary cilium is an antenna-like and non-motile structure protruding from the apical surface of most mammalian cells including endothelial cells lining the inner side of all the blood vessels in our body. Although it has been over a century since primary cilia were discovered, the investigation about their mechano-sensing and other roles in maintaining normal functions of cardiovascular system has just started in recent years. This focused review aims to give an update about the current literature for the role of endothelial primary cilia in blood flow mechano-sensing and shear stress-shielding. To do this, we first summarized the characteristic features… More >

  • Open Access


    Smart CardioWatch System for Patients with Cardiovascular Diseases Who Live Alone

    Raisa Nazir Ahmed Kazi1,*, Manjur Kolhar2, Faiza Rizwan2

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1237-1250, 2021, DOI:10.32604/cmc.2020.012707

    Abstract The widespread use of smartwatches has increased their specific and complementary activities in the health sector for patient’s prognosis. In this study, we propose a framework referred to as smart forecasting CardioWatch (SCW) to measure the heart-rate variation (HRV) for patients with myocardial infarction (MI) who live alone or are outside their homes. In this study, HRV is used as a vital alarming sign for patients with MI. The performance of the proposed framework is measured using machine learning and deep learning techniques, namely, support vector machine, logistic regression, and decision-tree classification techniques. The results indicated that the analysis of… More >

  • Open Access


    On the Image-Based Non-Invasive Diagnosis of Cardiovascular Diseases

    Peng Wu1,*, Qi Gao2, Runjie Wei3, Hongping Wang3, Lizhong Wang3

    Molecular & Cellular Biomechanics, Vol.16, Suppl.1, pp. 27-28, 2019, DOI:10.32604/mcb.2019.05711

    Abstract Cardiovascular diseases are the leading cause of human deaths worldwide. Traditional diagnostic tools of cardiovascular diseases are either based on 2D static medical images, or invasive, bringing troubles to both patients and doctors. Our team is committed to the development of image-based non-invasive diagnostic system for cardiovascular diseases. We have made progress mainly in the following areas: 1) 4D flow technology for heart and large blood vessels. According to MRI 4D Flow data, three-dimensional velocity fields within blood vessels were constructed. Divergence-fee smoothing (DFS) was proposed to eliminate the high frequency noise in the hemodynamic flow field, and make the… More >

  • Open Access


    Image-Based Computational Modeling for Cardiovascular Diseases with Potential Clinical Applications

    Dalin Tang1, Chun Yang2, Pedro N. del Nido3, Tal Geva3, Chun Yuan4, Tom Hatsukami5, Fei Liu4, Jie Zheng6, Pamela K. Woodard6

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.1, No.1, pp. 1-6, 2007, DOI:10.3970/icces.2007.001.001

    Abstract Image-based computational models for blood flow in the heart and diseased arteries have been developed for disease assessment and potential clinical applications. Models with fluid-structure interactions for human right ventricle (RV) remodeling surgery design, carotid and coronary atherosclerotic plaques and abdominal aortic aneurysm (AAA) were presented. Organ morphology, material properties, governing equations, proper initial and boundary conditions, controlling factors and research focuses for each model were discussed. More >

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