A new smart healthcare framework for real-time heart disease detection based on deep and machine learning

Elwahsh, Haitham; El-Shafeiy, Engy; Alanazi, Saad; Tawfeek, Medhat A.

doi:10.7717/peerj-cs.646

Cited by 16 publications

(7 citation statements)

References 20 publications

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“…Within this group, two subgroups stood out, the first of which did not name all the components used, particularly the sensor devices, but, rather, stated that they composed their own wearables from sensor devices. Thus, in [ 39 , 41 , 42 , 43 , 46 , 48 , 50 , 51 , 54 , 56 , 58 , 59 , 60 , 61 , 62 , 66 , 86 , 88 , 92 , 116 , 118 , 123 ], the authors proposed custom-built wearables with unspecified components. These studies were able to detect various cardiovascular diseases such as atrial fibrillation, atrial flutter, atrial premature contraction, atrial tachycardia, cardiac asystole, cardiovascular risk, fusion beats, heart attack, heart failure, hypertension, myocardial infarction, myocardial ischemia, premature atrial contractions, and premature ventricular contractions.…”

Section: Resultsmentioning

confidence: 99%

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Moshawrab

Adda

Bouzouane

et al. 2023

Sensors

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Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.

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Section: Resultsmentioning

confidence: 99%

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Moshawrab

Adda

Bouzouane

et al. 2023

Sensors

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“…Step 7: Update weight as in Equation (10) Step 8: Evaluate generalization error as in Equation (11) Step 9:…”

Section: Quadratic Weighted Entropy Boosting Classificationmentioning

confidence: 99%

“…A comprehensive survey was designed in (9) on the basis of artificial intelligence techniques for diagnosing several diseases like, Alzheimer, diabetes, heart disease, stroke, tuberculosis, and so on. Yet another smart healthcare framework was proposed in (10) on the basis of deep and machine learning using optimization stochastic gradient descent. In this method good accuracy was attained.…”

Section: Introductionmentioning

confidence: 99%

Big Data Analytics for Heart Disease Prediction using Regularized Principal and Quadratic Entropy Boosting

Muthulakshmi,

Parveen

2024

IJST

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Objectives: Over the past few years, there prevails an abundance wealth of big data obtained via patients' electronic health records. One of the leading causes of mortality globally is the cardiovascular disease. Based on the present test and history cardiovascular disease diagnosing of patients can be done. Therefore, early and quick diagnosis can reduce the mortality rate. To address their needs, several machine learning methods have been employed in the recent past in cardiovascular disease diagnosis and prediction. Previous research was also concentrated on acquiring the significant features to heart disease prediction however less importance was given to the time involved and error rate to identifying the strength of these features. Methods: In this work we plan to develop a method called, Regularized Principal Component and Quadratic Weighted Entropy Boosting (RPC-QWEB) for predicting heart disease. Initially in RPC-QWEB, relevant features are selected to avoid missing values in the input database by employing Regularized Principal Component Regressive Feature Selection (RPCRFS). Second, with the obtained dimensionality reduced features, Quadratic Weighted Entropy Boosting Classification (QWEBC) process is carried out to classify the patient data as normal or abnormal. The QWEBC process is an ensemble of several weak classifiers (i.e., Quadratic Classifier). The weak classifier results are combined to form strong classifier and provide final prediction results as normal or abnormal condition with minimal error rate. Findings: Experimental evaluation is carried out on factors with the cardiovascular disease dataset such as heart disease prediction accuracy, heart disease prediction time, sensitivity, error rate with respect to distinct numbers of patient data. The proposed RPC-QWEB method was compared with existing Heart Disease Prediction Framework (HDPF) and Swarm Artificial Neural Network (Swarm-ANN). Novelty: RPC-QWEB method outperforms the conventional learning methods in terms of numerous performance matrices. The RPC-QWEB method produces 3% and 5% increase in terms of accuracy and sensitivity and 7% and 29% reduced prediction time and error rate as compared to the existing benchmark methods. We may use this method to predict the heart disease at early stage there by we can reduce the death rate. Keywords: Big data, Regularized Principal Component, Quadratic Weighted Entropy Boosting, Regressive Feature Selection, Classification

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“…These applications are chosen considering the main real-life applications and medical sector. In the current covid situation the face mask detection is a socially relevant application ( Kumar et al, 2021 ), the pneumonia detection is a case from the medical field and disease detection ( Elwahsh et al, 2021 ) and the plant disease detection is from the agricultural sector.…”

Section: Introductionmentioning

confidence: 99%

Effect of neural network structure in accelerating performance and accuracy of a convolutional neural network with GPU/TPU for image analytics

Ravikumar

Sriraman

Saketh

et al. 2022

PeerJ Computer Science

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Background In deep learning the most significant breakthrough in the field of image recognition, object detection language processing was done by Convolutional Neural Network (CNN). Rapid growth in data and neural networks the performance of the DNN algorithms depends on the computation power and the storage capacity of the devices. Methods In this paper, the convolutional neural network used for various image applications was studied and its acceleration in the various platforms like CPU, GPU, TPU was done. The neural network structure and the computing power and characteristics of the GPU, TPU was analyzed and summarized, the effect of these on accelerating the tasks is also explained. Cross-platform comparison of the CNN was done using three image applications the face mask detection (object detection/Computer Vision), Virus Detection in Plants (Image Classification: agriculture sector), and Pneumonia detection from X-ray Images (Image Classification/medical field). Results The CNN implementation was done and a comprehensive comparison was done on the platforms to identify the performance, throughput, bottlenecks, and training time. The CNN layer-wise execution in GPU and TPU is explained with layer-wise analysis. The impact of the fully connected layer and convolutional layer on the network is analyzed. The challenges faced during the acceleration process were discussed and future works are identified.

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A new smart healthcare framework for real-time heart disease detection based on deep and machine learning

Cited by 16 publications

References 20 publications

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Big Data Analytics for Heart Disease Prediction using Regularized Principal and Quadratic Entropy Boosting

Effect of neural network structure in accelerating performance and accuracy of a convolutional neural network with GPU/TPU for image analytics

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