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  • 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 - 21 June 2023

    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 More > Graphic Abstract

    The role of periodontal disease in atherosclerotic cardiovascular disease

  • 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 - 29 April 2023

    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… 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 - 05 January 2023

    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

    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 - 31 October 2022

    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

    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 - 29 September 2022

    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

    ARTICLE

    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 - 09 December 2021

    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

    ARTICLE

    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 - 02 December 2021

    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

    ARTICLE

    Cardiovascular Disease Prediction Among the Malaysian Cohort Participants Using Electrocardiogram

    Mohd Zubir Suboh1,2, Nazrul Anuar Nayan1,3,*, Noraidatulakma Abdullah4,5, Nurul Ain Mhd Yusof4, Mariatul Akma Hamid4, Azwa Shawani Kamalul Arinfin4, Syakila Mohd Abd Daud4, Mohd Arman Kamaruddin4, Rosmina Jaafar1, Rahman Jamal4

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1111-1132, 2022, DOI:10.32604/cmc.2022.022123 - 03 November 2021

    Abstract A comprehensive study was conducted to differentiate cardiovascular disease (CVD) subjects from non-CVD subjects using short recording electrocardiogram (ECG) of 244 Malaysian adults in The Malaysian Cohort project. An automated peak detection algorithm to detect nine fiducial points of electrocardiogram (ECG) was developed. Forty-eight features were extracted in both time and frequency domains, including statistical features obtained from heart rate variability and Poincare plot analysis. These include five new features derived from spectrum counts of five different frequency ranges. Feature selection was then made based on p-value and correlation matrix. Selected features were used as… More >

  • Open Access

    ARTICLE

    Intelligent Biomedical Electrocardiogram Signal Processing for Cardiovascular Disease Diagnosis

    R. Krishnaswamy1,*, B. Sivakumar2, B. Viswanathan3, Fahd N. Al-Wesabi4,5, Marwa Obayya6, Anwer Mustafa Hilal7

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 255-268, 2022, DOI:10.32604/cmc.2022.021995 - 03 November 2021

    Abstract Automatic biomedical signal recognition is an important process for several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patterns of the ECG signals. In order to raise the diagnostic accuracy and reduce the diagnostic time, automated computer aided diagnosis model is necessary. With the advancements of artificial intelligence (AI) techniques, large quantity of biomedical datasets can be easily examined for decision making. In this aspect, this paper presents an intelligent biomedical ECG signal processing (IBECG-SP) technique for CVD diagnosis.… More >

  • Open Access

    ARTICLE

    Prediction of Cardiovascular Disease Using Machine Learning Technique—A Modern Approach

    Visvasam Devadoss Ambeth Kumar1, Chetan Swarup2, Indhumathi Murugan1, Abhishek Kumar3, Kamred Udham Singh4, Teekam Singh5, Ramu Dubey6,*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 855-869, 2022, DOI:10.32604/cmc.2022.021582 - 03 November 2021

    Abstract Cardio Vascular disease (CVD), involving the heart and blood vessels is one of the most leading causes of death throughout the world. There are several risk factors for causing heart diseases like sedentary lifestyle, unhealthy diet, obesity, diabetes, hypertension, smoking and consumption of alcohol, stress, hereditary factory etc. Predicting cardiovascular disease and improving and treating the risk factors at an early stage are of paramount importance to save the precious life of a human being. At present, the highly stressful life with bad lifestyle activities causes heart disease at a very young age. The main… More >

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