Evolutionary algorithm-based convolutional neural network for predicting heart diseases

Samir, Ali A.; Rashwan, Abdullah R.; Sallam, Karam M.; Chakrabortty, Ripon K.; Ryan, Michael J.; Abohany, Amr A.

doi:10.1016/j.cie.2021.107651

Cited by 19 publications

(19 citation statements)

References 68 publications

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“…In this section, the performance of the AWMYolov4 Model and the existing related approaches namely DBN [1] and RCNN model [2] are discussed with some metrics, prediction accuracy, classification Error, False Positive Rate, Precision, Sensitivity, F1 Score and specificity. True positive is TP, False positive is FP, True Negative is TN, and False Negative is FN.…”

Section: Resultsmentioning

confidence: 99%

“…Following that, the features are retrieved to quickly and accurately do classification for heart disease prediction. Therefore, compared to DBN [1] and RCNN model [2], the suggested AWMYoloV4 Model reduces prediction time by 30% and 15%, respectively. Figure 4 shows a graphical representation of forecast time.…”

Section: Prediction Timementioning

confidence: 99%

“…In this section, the performance of the AWMYolov4 Model and the existing related approaches namely DBN [1] and RCNN model [2] are discussed with three metrics, prediction accuracy, classification Error, Model loss, Precision, Sensitivity and F1 Score.In this paper, Python 3.5 is used as the programming language. For the training, there were a total of 10,000 iterations, and it took 6 hours and 50 minutes.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…y(w+r) = f(t0 ,…., tw, Xi,0, …,Xi,w,tw+r,Yw+r) (7) y(w+r) = f(t0 ,…., tw, Xi,0, …,Xi,w, Y0,…Yw, tw+r,Yw+r) (8) What our function will do is to flatten all the information contained in the window, which is all the values inside the W window, and the timestamp(s) of when we want to make the prediction. standard approaches [1] and [2], which have prediction times of 70% and 80%, respectively, the AWMYoloV4 Model's observed prediction time is 40%. For each technique, varied prediction time results are obtained.…”

Section: Prediction Timementioning

confidence: 99%

“…Upon comprehensively reviewing the current high-tech approaches, we found the various models to summarized in Table 1, that is Art and state for Critical analysis and comparison of study various characteristics, model/algorithm and the result. [2,3] As a result of noise reduction and muscle removal methods not being included in most classification techniques, a high rate of false positives and misclassifications occurs. A variety of techniques is available for classifying cardio images and extracting different features.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An Implementation of Cardiovascular Disease Prediction in Ultrasonography Images using AWMYOLOv4 Deep Learning Mode

Damodharan

Goel

2022

IJRITCC

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Cardiovascular diseases are one of the most important issues facing the people and their origins also death is contained all over the world the facing issues in past 25 years. Every country’s inversing large amount in health care researches and it’s related to enhanced predict the diseases. Cardio issues are not even physicians can easily be predicted and it is a very challenging task that requires high knowledge and expertise. To identify to create machine language models used to efficiently predict the earliest stage of cardiovascular disease. In this work, we recommend AWMF filter for the pre-process the Input Image after the input move to YOLOv4 neural network method for classification and segmentation to the heart affected areas by using ultrasonic Images with the help of a machine learning algorithm. The proposed algorithm uses ultrasonic picture classification and segmentation to detect cardiovascular disease earlier. This model shows the more accurate result on 96% of training and 98% testing data. And this method shows better results and providing while compared to the existing method.

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Section: Resultsmentioning

confidence: 99%

Section: Prediction Timementioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Prediction Timementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Implementation of Cardiovascular Disease Prediction in Ultrasonography Images using AWMYOLOv4 Deep Learning Mode

Damodharan

Goel

2022

IJRITCC

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Estimation of coconut maturity based on fuzzy neural network and sperm whale optimization

El-Shafeiy

Abohany

Elmessery

et al. 2023

Neural Comput & Applic

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Coconut water is the clear liquid found inside coconuts, famous for rehydrating after exercise or while suffering from a minor sickness. The essential issue tackled in this paper is how to estimate the appropriate stage of maturity of coconut water, which is a time-consuming task in the beverage industry since, as the coconut age increases, the coconut water flavor varies. Accordingly, to handle this issue, an adaptive model based on Fuzzy Neural Network and Sperm Whale Optimization, dubbed FNN–SWO, is developed to assess coconut water maturity. The Sperm Whale Optimization (SWO) algorithm is a meta-heuristic optimization algorithm. It is embedded in this model along with neural networks and fuzzy techniques (FNN system), which can be employed as an essential building block in the beverage industry. The proposed FNN–SWO model is trained and tested utilizing fuzzy rules with an adaptive network. In contrast, the SWO algorithm is adopted to determine the optimal weights for the fuzzy rules. Three subsets of data divided according to three levels of coconut water maturity-tender, mature, and very mature, are used to validate the combined FNN–SWO model. Depending on these three subsets of data, a comparison of the proposed FNN–SWO model has been conducted against a set of the most common conventional techniques. These techniques include Support Vector Machine, Naïve Bayes, FNN, Artificial Neural Network, as well as their embedding with other meta-heuristic optimization algorithms. For various key performance indicators, such as recall, F1-score, specificity, and accuracy, the proposed FNN–SWO model provides the best prediction outcomes compared to the current time-consuming techniques. The dominance of the proposed FNN–SWO model is evident from the final findings compared to its time-consuming peers for estimating coconut water maturity on time. As a result, the proposed FNN–SWO model is an effective heuristic for locating optimal solutions to classification problems. It can thereby be reassuringly applicable to other similar prediction problems. Additionally, it would benefit the scientific community interested in evaluating coconut water.

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Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review

Kaveh

Mesgari

2022

Neural Process Lett

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The learning process and hyper-parameter optimization of artificial neural networks (ANNs) and deep learning (DL) architectures is considered one of the most challenging machine learning problems. Several past studies have used gradient-based back propagation methods to train DL architectures. However, gradient-based methods have major drawbacks such as stucking at local minimums in multi-objective cost functions, expensive execution time due to calculating gradient information with thousands of iterations and needing the cost functions to be continuous. Since training the ANNs and DLs is an NP-hard optimization problem, their structure and parameters optimization using the meta-heuristic (MH) algorithms has been considerably raised. MH algorithms can accurately formulate the optimal estimation of DL components (such as hyper-parameter, weights, number of layers, number of neurons, learning rate, etc.). This paper provides a comprehensive review of the optimization of ANNs and DLs using MH algorithms. In this paper, we have reviewed the latest developments in the use of MH algorithms in the DL and ANN methods, presented their disadvantages and advantages, and pointed out some research directions to fill the gaps between MHs and DL methods. Moreover, it has been explained that the evolutionary hybrid architecture still has limited applicability in the literature. Also, this paper classifies the latest MH algorithms in the literature to demonstrate their effectiveness in DL and ANN training for various applications. Most researchers tend to extend novel hybrid algorithms by combining MHs to optimize the hyper-parameters of DLs and ANNs. The development of hybrid MHs helps improving algorithms performance and capable of solving complex optimization problems. In general, the optimal performance of the MHs should be able to achieve a suitable trade-off between exploration and exploitation features. Hence, this paper tries to summarize various MH algorithms in terms of the convergence trend, exploration, exploitation, and the ability to avoid local minima. The integration of MH with DLs is expected to accelerate the training process in the coming few years. However, relevant publications in this way are still rare.

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Evolutionary algorithm-based convolutional neural network for predicting heart diseases

Cited by 19 publications

References 68 publications

An Implementation of Cardiovascular Disease Prediction in Ultrasonography Images using AWMYOLOv4 Deep Learning Mode

An Implementation of Cardiovascular Disease Prediction in Ultrasonography Images using AWMYOLOv4 Deep Learning Mode

Estimation of coconut maturity based on fuzzy neural network and sperm whale optimization

Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review

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