RETRACTED ARTICLE: Accuracy detection of coronary artery disease using machine learning algorithms

Singh, Harinder; Bano, Tasneem; Gangadhar, Ch.; Anand, Rohit; Sindhwani, Nidhi; Babu, Madhu

doi:10.1007/s13204-021-02036-7

Cited by 33 publications

(3 citation statements)

References 10 publications

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“…However, several limitations have been addressed in the related work. For instance, a lack of patients with low risk of CAD in the data, machine learning methods which depend on the quality of data, and the requirement for extensive batch-based training [ 24 , 38 ]. Nevertheless, several limitations were encountered in our study.…”

Section: Discussionmentioning

confidence: 99%

A Coronary Artery Disease Monitoring Model Built from Clinical Data and Alpha-1-Antichymotrypsin

et al. 2022

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Coronary artery disease (CAD) is one of the most common subtypes of cardiovascular disease. The progression of CAD initiates from the plaque of atherosclerosis and coronary artery stenosis, and eventually turns into acute myocardial infarction (AMI) or stable CAD. Alpha-1-antichymotrypsin (AACT) has been highly associated with cardiac events. In this study, we proposed incorporating clinical data on AACT levels to establish a model for estimating the severity of CAD. Thirty-six healthy controls (HCs) and 162 CAD patients with stenosis rates of <30%, 30–70%, and >70% were included in this study. Plasma concentration of AACT was determined by enzyme-linked immunosorbent assay (ELISA). The receiver operating characteristic (ROC) curve analysis and associations were conducted. Further, five machine learning models, including decision tree, random forest, support vector machine, XGBoost, and lightGBM were implemented. The lightGBM model obtained a sensitivity of 81.4%, a specificity of 67.3%, and an area under the curve (AUC) of 0.822 for identifying CAD patients with a stenosis rate of <30% versus >30%. In this study, we provided a demonstration of a monitoring model with clinical data and AACT.

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Section: Discussionmentioning

confidence: 99%

A Coronary Artery Disease Monitoring Model Built from Clinical Data and Alpha-1-Antichymotrypsin

et al. 2022

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“…So, the side faces and small faces pixels in the image are included. Currently, convolution neural networks (CNNs) [ 24 , 25 ] have attained better outcomes in computer vision, such as recognition of an object and image labeling [ 26 ]. For this reason, the scientists also employ them to implement some algorithms in facial recognition [ 27 ], such as R-CNN, fast R-CNN, and faster R-CNN.…”

Section: The Proposed System Modelmentioning

confidence: 99%

Mask Detection and Social Distance Identification Using Internet of Things and Faster R-CNN Algorithm

Meivel¹,

Sindhwani

Anand³

et al. 2022

Computational Intelligence and Neuroscience

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The drones can be used to detect a group of people who are unmasked and do not maintain social distance. In this paper, a deep learning-enabled drone is designed for mask detection and social distance monitoring. A drone is one of the unmanned systems that can be automated. This system mainly focuses on Industrial Internet of Things (IIoT) monitoring using Raspberry Pi 4. This drone automation system sends alerts to the people via speaker for maintaining the social distance. This system captures images and detects unmasked persons using faster regions with convolutional neural network (faster R-CNN) model. When the system detects unmasked persons, it sends their details to respective authorities and the nearest police station. The built model covers the majority of face detection using different benchmark datasets. OpenCV camera utilizes 24/7 service reports on a daily basis using Raspberry Pi 4 and a faster R-CNN algorithm.

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“…Machine learning is a modern technology that expands with a variety of implementations and can be one of the main components of intelligent information systems. It requires compact generalizations derived from vast repositories of datasets of historical information to be used as knowledge in various practical ways; for example, it is used in automated processes such as systems of experts or explicitly used to communicate with the experts of humans and for educational purposes [ 2 ]. Diabetes is one of the most severe challenges in the health sectors facing most developed and developing countries today.…”

Section: Introductionmentioning

confidence: 99%

Diabetes disease prediction system using HNB classifier based on discretization method

Alsewari

Basurra

Bhogal

et al. 2023

Journal of Integrative Bioinformatics

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Diagnosing diabetes early is critical as it helps patients live with the disease in a healthy way – through healthy eating, taking appropriate medical doses, and making patients more vigilant in their movements/activities to avoid wounds that are difficult to heal for diabetic patients. Data mining techniques are typically used to detect diabetes with high confidence to avoid misdiagnoses with other chronic diseases whose symptoms are similar to diabetes. Hidden Naïve Bayes is one of the algorithms for classification, which works under a data-mining model based on the assumption of conditional independence of the traditional Naïve Bayes. The results from this research study, which was conducted on the Pima Indian Diabetes (PID) dataset collection, show that the prediction accuracy of the HNB classifier achieved 82%. As a result, the discretization method increases the performance and accuracy of the HNB classifier.

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RETRACTED ARTICLE: Accuracy detection of coronary artery disease using machine learning algorithms

Cited by 33 publications

References 10 publications

A Coronary Artery Disease Monitoring Model Built from Clinical Data and Alpha-1-Antichymotrypsin

A Coronary Artery Disease Monitoring Model Built from Clinical Data and Alpha-1-Antichymotrypsin

Mask Detection and Social Distance Identification Using Internet of Things and Faster R-CNN Algorithm

Diabetes disease prediction system using HNB classifier based on discretization method

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