Cardiac Stroke Prediction Framework using Hybrid Optimization Algorithm under DNN

Alshammari, Nawaf; Alzamil, Ahmed; Albadarn, M.; Ahmed, Sobhi; Syed, M. B.; Alshammari, Ahmed S.; Gabr, Ahmed Maher

doi:10.48084/etasr.4277

Cited by 15 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies on predictive algorithms for healthcare system construction also have mentioned high ACC in DNN. [53][54][55][56] In addition, the proposed DNN with a quantile scaler and SVC with a quantile scaler outperformed other combinations. As the SVC is generally used for binary classification, it might show better performance than others except for DNN.…”

Section: Discussionmentioning

confidence: 92%

See 1 more Smart Citation

A smart healthcare-based system for classification of dementia using deep learning

Lim

2022

DIGITAL HEALTH

View full text Add to dashboard Cite

Objectives This study aims to develop a deep learning-based classification model for early detection of dementia using a wearable device that can measure the electrical conductivity of the skin, temperature, and movement as factors related to dementia, interlocking them with an application, and analyzing the collected data. Methods This study was conducted on 18 elderly individuals (5 males, 13 females) aged 65 years or older who consented to the study. The Korean Mini-Mental State Examination survey for cognitive function tests was conducted by well-trained researchers. The subjects were first grouped into high- or low-risk group for dementia based on their Korean Mini-Mental State Examination score. Data obtained by wearable devices of each subject were then used for the classification of the high- and low-risk groups of dementia through a smart healthcare-based system implementing a deep neural network with scaled principal component analysis. The correlation coefficients between the Korean Mini-Mental State Examination score and the featured data were also investigated. Results Our study showed that the proposed system using a deep neural network with scaled principal component analysis was effective in detecting individuals at high risk for dementia with up to 99% accuracy and which performance was better compared with commonly used classification algorithms. In addition, it was found that the electrical conductivity of skin had the closest correlation with the results of the Korean Mini-Mental State Examination score among data collected through wearable devices in this study. Conclusions Our proposed system can contribute to effective early detection of dementia for the elderly, using a non-invasive and easy-to-wear wearable device and classification algorithms with a simple cognitive function test. In the future, we intend to have more subjects participate in the experiment, to include more relevant variables in the wearable device, and to analyze the effectiveness of the smart healthcare-based dementia classification system over the long term.

show abstract

Section: Discussionmentioning

confidence: 92%

“…Previous studies on predictive algorithms for healthcare system construction also have mentioned high ACC in DNN. 53 – 56 …”

Section: Discussionmentioning

confidence: 99%

A smart healthcare-based system for classification of dementia using deep learning

Lim

2022

DIGITAL HEALTH

View full text Add to dashboard Cite

show abstract

“…There are different methods using different datasets such as Kaggle, Kaggle electronic medical records (Kaggle EMR), 2D CT dataset, and CT image dataset that have been applied to the task of stroke classification. Because it is difficult to modify results using different datasets, we have included current results using the CT image dataset [51][52][53][54].…”

Section: Experiments Resultsmentioning

confidence: 99%

Deep Learning-Enabled Brain Stroke Classification on Computed Tomography營mages

Tursynova¹,

Omarov²,

Tukenova³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

In the field of stroke imaging, deep learning (DL) has enormous untapped potential. When clinically significant symptoms of a cerebral stroke are detected, it is crucial to make an urgent diagnosis using available imaging techniques such as computed tomography (CT) scans. The purpose of this work is to classify brain CT images as normal, surviving ischemia or cerebral hemorrhage based on the convolutional neural network (CNN) model. In this study, we propose a computer-aided diagnostic system (CAD) for categorizing cerebral strokes using computed tomography images. Horizontal flip data magnification techniques were used to obtain more accurate categorization. Image Data Generator to magnify the image in real time and apply any random transformations to each training image. An early stopping method to avoid overtraining. As a result, the proposed methods improved several estimation parameters such as accuracy and recall, compared to other machine learning methods. A python web application was created to demonstrate the results of CNN model classification using cloud development techniques. In our case, the model correctly identified the drawing class as normal with 79% accuracy. Based on the collected results, it was determined that the presented automated diagnostic system could be used to assist medical professionals in detecting and classifying brain strokes.

show abstract

“…Although adding hidden layers to a model during training can improve its effectiveness, doing so has a price. That price is in the form of processing time, the complexity of the model, and the prediction accuracy [25,26]. Equation (1) can be used to formalize the DNN model as…”

Section: Proposed Pco-dnn Approach For Cardiovascular Disease Predictionmentioning

confidence: 99%

Deep Neural Network and Predator Crow Optimization-Based Intelligent Healthcare System for Predicting Cardiac Diseases

Alqurashi,

Zafar,

Khan

et al. 2023

Mathematics

View full text Add to dashboard Cite

Cardiovascular diseases (CVD) are amongst the leading causes of death worldwide. The Internet of Things (IoT) is an emerging technology that enables the healthcare system to identify cardiovascular diseases. In this article, a novel cardiovascular disease prediction framework combining Predator Crow Optimization (PCO) and Deep Neural Network (DNN) is designed. In the proposed PCO-DNN framework, DNN is used to predict cardiac disease, and the PCO is utilized to optimize the DNN parameters, thereby maximizing the prediction performances. The proposed framework aims to predict and classify cardiovascular diseases accurately. Further, an intensive comparative analysis is performed to validate the obtained results with the existing classification models. The results show that the proposed framework achieves an accuracy of 96.6665%, a precision of 97.5256%, a recall of 97.0953%, and an F1-measure of 96.4242% and can outperform the existing CVD predictors.

show abstract

Cardiac Stroke Prediction Framework using Hybrid Optimization Algorithm under DNN

Cited by 15 publications

References 21 publications

A smart healthcare-based system for classification of dementia using deep learning

A smart healthcare-based system for classification of dementia using deep learning

Deep Learning-Enabled Brain Stroke Classification on Computed Tomography營mages

Deep Neural Network and Predator Crow Optimization-Based Intelligent Healthcare System for Predicting Cardiac Diseases

Contact Info

Product

Resources

About