A smart decision support system to diagnose arrhythymia using ensembled ConvNet and ConvNet-LSTM model

Tiwari, Shamik; Jain, Anurag; Sapra, Varun; Koundal, Deepika; Alenezi, Fayadh; Polat, Kemal; Alhudhaif, Adi

doi:10.1016/j.eswa.2022.118933

Cited by 36 publications

(26 citation statements)

References 42 publications

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“…As a result of the inadequate hardware, the layers of traditional neural networks can be constrained by the learned parameters, and the coordination that exists within the layers requires a highly extensive computation machine. Because high‐end machines are now more readily available, it is now feasible to train deep models by employing neural networks with several layers 42–45 …”

Section: Methodsmentioning

confidence: 99%

“…Because high-end machines are now more readily available, it is now feasible to train deep models by employing neural networks with several layers. [42][43][44][45] The capacity of the CNN algorithms to extract features drastically reduces the amount of time required for pre-processing, such as image annotation. This is one of the significant advantages of the algorithm.…”

Section: Methodsmentioning

confidence: 99%

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Using a novel convolutional neural network for plant pests detection and disease classification

et al. 2023

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Background Early plant diseases and pests identification reduces social, economic, and environmental deficiencies entailing toxic chemical utilization on agricultural farms, thus posing a threat to global food security. Methodology An enhanced convolutional neural network (CNN) along with long short‐term memory (LSTM) using a majority voting ensemble classifier has been proposed to tackle plant pest and disease identification and classification. Within pre‐trained models, deep feature extractions have been obtained from connected layers. Deep features have been extracted and are sent to the LSTM layer to build a robust, enhanced LSTM‐CNN model for detecting plant pests and diseases. Experiments were carried out using a Turkey dataset, with 4447 apple pests and diseases categorized into 15 different classes. Results The study was evaluated in different CNNs using logistic regression (LR), LSTM, and extreme learning machine (ELM), focusing on plant disease detection problems. The ensemble majority voting classifier was used at the LSTM layer to detect and classify plant disease labels. Furthermore, an autonomous selection of the optimal LSTM layer network parameters was applied. Finally, the performance was validated based on sensitivity, F1 score, accuracy, and specificity using LSTM, ELM, and LR classifiers. Conclusion The presented model attained 99.2% accuracy compared to the cutting‐edge models on different classifiers such as LSTM, LR, and ELM, and performed better compared to transfer learning. Pre‐trained models, such as VGG19, VGG18, and AlexNet, demonstrated better accuracy when the fc6 layer was compared with other layers. © 2023 Society of Chemical Industry.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Using a novel convolutional neural network for plant pests detection and disease classification

et al. 2023

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“…This is called ensemble learning, which refers to algorithms that combine the predictions from two or more models. Ensemble learning has been applied in the medical field and shows promising results in QRS complex detection and classification, as well as arrhythmia detection [ 26 , 27 ]. In other words, a pruned CNN network is kept to extract features, and then the classification is performed based on SVM.…”

Section: Methodsmentioning

confidence: 99%

CNN and SVM-Based Models for the Detection of Heart Failure Using Electrocardiogram Signals

Botros

Mourad-Chehade

Laplanche

2022

Sensors

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Heart failure (HF) is a serious condition in which the heart fails to supply the body with enough oxygen and nutrients to function normally. Early and accurate detection of heart failure is critical for impeding disease progression. An electrocardiogram (ECG) is a test that records the rhythm and electrical activity of the heart and is used to detect HF. It is used to look for irregularities in the heart’s rhythm or electrical conduction, as well as a history of heart attacks, ischemia, and other conditions that may initiate HF. However, sometimes, it becomes difficult and time-consuming to interpret the ECG signal, even for a cardiac expert. This paper proposes two models to automatically detect HF from ECG signals: the first one introduces a Convolutional Neural Network (CNN), while the second one suggests an extension of it by integrating a Support Vector Machine (SVM) layer for the classification at the end of the network. The proposed models provide a more accurate automatic HF detection using 2-s ECG fragments. Both models are smaller than previously proposed models in the literature when the architecture is taken into account, reducing both training time and memory consumption. The MIT-BIH and the BIDMC databases are used for training and testing the adopted models. The experimental results demonstrate the effectiveness of the proposed framework by achieving an accuracy, sensitivity, and specificity of over 99% with blindfold cross-validation. The models proposed in this study can provide doctors with reliable references and can be used in portable devices to enable the real-time monitoring of patients.

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“…However, none of the researchers implemented a feature selection framework to dynamically select the features for the model; rather, they selected the features based on suggestions from different scholars in the literature. ML has also been applied in different medical contexts to improve the prediction of other diseases and has recorded a huge increase in accurate results [ 28 , 29 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Generic Framework for Heart Problem Diagnosis Based on a Machine Learning Paradigm

Menshawi

Hassan

Allheeib

et al. 2023

Sensors

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The early, valid prediction of heart problems would minimize life threats and save lives, while lack of prediction and false diagnosis can be fatal. Addressing a single dataset alone to build a machine learning model for the identification of heart problems is not practical because each country and hospital has its own data schema, structure, and quality. On this basis, a generic framework has been built for heart problem diagnosis. This framework is a hybrid framework that employs multiple machine learning and deep learning techniques and votes for the best outcome based on a novel voting technique with the intention to remove bias from the model. The framework contains two consequent layers. The first layer contains simultaneous machine learning models running over a given dataset. The second layer consolidates the outputs of the first layer and classifies them as a second classification layer based on novel voting techniques. Prior to the classification process, the framework selects the top features using a proposed feature selection framework. It starts by filtering the columns using multiple feature selection methods and considers the top common features selected. Results from the proposed framework, with 95.6% accuracy, show its superiority over the single machine learning model, classical stacking technique, and traditional voting technique. The main contribution of this work is to demonstrate how the prediction probabilities of multiple models can be exploited for the purpose of creating another layer for final output; this step neutralizes any model bias. Another experimental contribution is proving the complete pipeline’s ability to be retrained and used for other datasets collected using different measurements and with different distributions.

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A smart decision support system to diagnose arrhythymia using ensembled ConvNet and ConvNet-LSTM model

Cited by 36 publications

References 42 publications

Using a novel convolutional neural network for plant pests detection and disease classification

Using a novel convolutional neural network for plant pests detection and disease classification

CNN and SVM-Based Models for the Detection of Heart Failure Using Electrocardiogram Signals

A Hybrid Generic Framework for Heart Problem Diagnosis Based on a Machine Learning Paradigm

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