A New Automatic Identification Method of Heart Failure Using Improved Support Vector Machine Based on Duality Optimization Technique

Geweid, Gamal G. N.; Abdallah, Magda A.

doi:10.1109/access.2019.2945527

Cited by 40 publications

(18 citation statements)

References 29 publications

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“…The COVID-19 detection analysis was conducted based on 10 numbers of population and 25 maximum numbers iterations. The proposed meta-heuristic algorithm was examined with additional algorithms like "Particle Swarm Optimization (PSO) [20], Grey Wolf Optimizer (GWO) [9], Whale Optimization Algorithm (WOA) [35], TSA [32], classifiers such as Support Vector Machine (SVM) [11], Auto encoder [43], Naive Bayes [5], Ensemble learning [24], RNN [42], LSTM [23] and SA-TSA".…”

Section: Methodsmentioning

confidence: 99%

“…There are three classifiers used in the EL-CNN-DF based detection, namely (a) SVM, (b) Autoencoder, and (c) NB, in which the ranking strategy is used to get the efficient result in the final decision regarding the detection of COVID-19 by taking the input as the extracted deep features E df from the pooling layer of CNN. (a) SVM [ 11 ]: The proposed model uses the SVM classifier to achieve improved efficiency. It is used to reduce the unseen errors or generalization errors caused by machine learning data.…”

Section: Ensemble Learning With Cnn-based Deep Features For Covid-19 Detectionmentioning

confidence: 99%

“…(a) SVM [ 11 ]: The proposed model uses the SVM classifier to achieve improved efficiency. It is used to reduce the unseen errors or generalization errors caused by machine learning data.…”

Section: Ensemble Learning With Cnn-based Deep Features For Covid-19 Detectionmentioning

confidence: 99%

“…The calculated probability distribution P(E df ) over the set of features is formulated in the Eq. (11).…”

Section: Developed El-cnn-df-based Detectionmentioning

confidence: 99%

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Adaptive UNet-based Lung Segmentation and Ensemble Learning with CNN-based Deep Features for Automated COVID-19 Diagnosis

Das¹

2021

Multimed Tools Appl

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COVID-19 disease is a major health calamity in twentieth century, in which the infection is spreading at the global level. Developing countries like Bangladesh, India, and others are still facing a delay in recognizing COVID-19 cases. Hence, there is a need for immediate recognition with perfect identification of infection. This clear visualization helps to save the life of suspected COVID-19 patients. With the help of traditional RT-PCR testing, the combination of medical images and deep learning classifiers delivers more hopeful results with high accuracy in the prediction and recognition of COVID-19 cases. COVID-19 disease is recently researched through sample chest X-ray images, which have already proven its efficiency in lung diseases. To emphasize corona virus testing methods and to control the community spreading, the automatic detection process of COVID-19 is processed through the detailed medication reports from medical images. Although there are numerous challenges in the manual understanding of traces in COVID-19 infection from X-ray, the subtle differences among normal and infected X-rays can be traced by the data patterns of Convolutional Neural Network (CNN). To improve the detection performance of CNN, this paper plans to develop an Ensemble Learning with CNN-based Deep Features (EL-CNN-DF). In the initial phase, image scaling and median filtering perform the pre-processing of the chest X-ray images gathered from the benchmark source. The second phase is lung segmentation, which is the significant step for COVID detection. It is accomplished by the Adaptive Activation Function-based U-Net (AAF-U-Net). Once the lungs are segmented, it is subjected to novel EL-CNN-DF, in which the deep features are extracted from the pooling layer of CNN, and the fully connected layer of CNN are replaced with the three classifiers termed “Support Vector Machine (SVM), Autoencoder, Naive Bayes (NB)”. The final detection of COVID-19 is done by these classifiers, in which high ranking strategy is utilized. As a modification, a Self Adaptive-Tunicate Swarm Algorithm (SA-TSA) is adopted as a boosting algorithm to enhance the performance of segmentation and detection. The overall analysis has shown that the precision of the enhanced CNN by using SA-TSA was 1.02%, 4.63%, 3.38%, 1.62%, 1.51% and 1.04% better than SVM, autoencoder, NB, Ensemble, RNN and LSTM respectively. The comparative performance analysis on existing model proves that the proposed algorithm is better than other algorithms in terms of segmentation and classification of COVID-19 detection.

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Section: Methodsmentioning

confidence: 99%

Section: Ensemble Learning With Cnn-based Deep Features For Covid-19 Detectionmentioning

confidence: 99%

“…(a) SVM [ 11 ]: The proposed model uses the SVM classifier to achieve improved efficiency. It is used to reduce the unseen errors or generalization errors caused by machine learning data.…”

Section: Ensemble Learning With Cnn-based Deep Features For Covid-19 Detectionmentioning

confidence: 99%

“…The calculated probability distribution P(E df ) over the set of features is formulated in the Eq. (11).…”

Section: Developed El-cnn-df-based Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive UNet-based Lung Segmentation and Ensemble Learning with CNN-based Deep Features for Automated COVID-19 Diagnosis

Das¹

2021

Multimed Tools Appl

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“…al. [28] designed HD identification techniques by using improved SVM based duality optimization technique. In the above literature the proposed HD diagnosis methods limitation and advantages have been summarized in Table 1 for better understanding the important of our proposed approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare

et al. 2020

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Heart disease is one of the complex diseases and globally many people suffered from this disease. On time and efficient identification of heart disease plays a key role in healthcare, particularly in the field of cardiology. In this article, we proposed an efficient and accurate system to diagnosis heart disease and the system is based on machine learning techniques. The system is developed based on classification algorithms includes Support vector machine, Logistic regression, Artificial neural network, K-nearest neighbor, Naïve bays, and Decision tree while standard features selection algorithms have been used such as Relief, Minimal redundancy maximal relevance, Least absolute shrinkage selection operator and Local learning for removing irrelevant and redundant features. We also proposed novel fast conditional mutual information feature selection algorithm to solve feature selection problem. The features selection algorithms are used for features selection to increase the classification accuracy and reduce the execution time of classification system. Furthermore, the leave one subject out cross-validation method has been used for learning the best practices of model assessment and for hyperparameter tuning. The performance measuring metrics are used for assessment of the performances of the classifiers. The performances of the classifiers have been checked on the selected features as selected by features selection algorithms. The experimental results show that the proposed feature selection algorithm (FCMIM) is feasible with classifier support vector machine for designing a high-level intelligent system to identify heart disease. The suggested diagnosis system (FCMIM-SVM) achieved good accuracy as compared to previously proposed methods. Additionally, the proposed system can easily be implemented in healthcare for the identification of heart disease.

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A Review on Machine Learning and Blockchain Technology in E-Healthcare

Tenepalli,

Thandava Meganathan

2023

Lecture Notes in Networks and Systems

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A New Automatic Identification Method of Heart Failure Using Improved Support Vector Machine Based on Duality Optimization Technique

Cited by 40 publications

References 29 publications

Adaptive UNet-based Lung Segmentation and Ensemble Learning with CNN-based Deep Features for Automated COVID-19 Diagnosis

Adaptive UNet-based Lung Segmentation and Ensemble Learning with CNN-based Deep Features for Automated COVID-19 Diagnosis

Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare

A Review on Machine Learning and Blockchain Technology in E-Healthcare

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