Cheng Wang¹, Haoran Zhu^2,*, Congjun Rao^2,*

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 >

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

Muhammad Tayyeb¹, Muhammad Umer¹, Khaled Alnowaiser², Saima Sadiq³, Ala’ Abdulmajid Eshmawi⁴, Rizwan Majeed⁵, Abdullah Mohamed⁶, Houbing Song⁷, Imran Ashraf^8,*

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 >

XIWEI ZHAO^1,#, JINSONG WANG^1,2,#, YIFAN XU¹, JIAN ZHOU^5,*, LEI HU^1,3,4,*

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

Ingo Paetsch^1,*, 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 >

Wasif Akbar¹, Adbul Mannan², Qaisar Shaheen^3,*, 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 >

Qaisar Abbas¹, Abdul Rauf Baig^1,*, 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 >

B. Karthikeyan^1,*, 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 >

Hanan Abdullah Mengash¹, Lubna A. Alharbi², Saud S. Alotaibi³, Sarab AlMuhaideb⁴, Nadhem Nemri⁵, Mrim M. Alnfiai⁶, Radwa Marzouk¹, Ahmed S. Salama⁷, Mesfer Al Duhayyim^8,*

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 >

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 >