Effective prediction of heart disease using hybrid ensemble deep learning and tunicate swarm algorithm

Wankhede, Jaishri; Sambandam, Palaniappan; Kumar, M. Naveen

doi:10.1080/07391102.2021.1987328

Cited by 9 publications

(7 citation statements)

References 37 publications

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“…This experimental analysis was undergone with a population count of 10 and maximum iterations of 25 for the proposed pest identification and classification model. The proposed AHBA-CNLSTM was compared with other meta-heuristic algorithms like "Particle Swarm Optimization (PSO) [27], Tunicate Swarm Algorithm (TSA) [28], Deer Hunting Optimization Algorithm (DHOA) [29], HBA [26] and deep learning algorithms like CNN [7], deep-CNN [6], RCNN [5] and LSTM [2]".…”

Section: Resultsmentioning

confidence: 99%

IoT-Enabled Pest Identification and Classification with New Meta-Heuristic-Based Deep Learning Framework

Kathole¹,

Vhatkar²,

Patil³

2022

Cybernetics and Systems

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The insect pests and crop diseases are the most critical factors that affect agricultural production, which reduces the sustainable development of agriculture. While detecting the pest, it is inconsistent to place the surveillance cameras near the target pests and the captured images from the Internet of Things (IoT) monitoring equipment at a constant location that is mostly insufficient for pest detection. IoT is a well-known advanced technology and an analytics system incorporated in diverse industries based on its unique abilities and flexibilities over a particular environment like agriculture. There is a demand for the IoT in agricultural areas to reduce the chemical crop protection agents and fertilizers to manage the efficient crop state and crop production. Hence, the data collection is through the IoT devices in this research model. This research aims to develop a pest identification and classification model to detect and identify the pests in the images. Initially, the IoT platform is created, and IoT devices conduct the data collection. Then, object detection is performed using Yolov3 to detect the pests in the images from the gathered images. The detected images are subjected to the Convolutional Neural Network (CNN) for gathering the deep features, which are then forwarded to the enhanced classifier, termed Convolution Neural Long Short-Term Memory (CNLSTM) for getting the classified outcomes as pest details, in which the optimization of parameters is done by Adaptive Honey Badger Algorithm (AHBA). These results demonstrated that the proposed method shows enhanced performance by rapidly collecting the information in agriculture and ensures the technical indication for population estimation and pest monitoring.

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Section: Resultsmentioning

confidence: 99%

IoT-Enabled Pest Identification and Classification with New Meta-Heuristic-Based Deep Learning Framework

Kathole¹,

Vhatkar²,

Patil³

2022

Cybernetics and Systems

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“…An innovative way of predicting heart-disease risk using a hybrid GA and PSO approach is presented in this paper [38]. For precise cardiac disease prediction, a Hybrid Trunicate Swarm Algorithm and Ensemble Deep Learning (TSA-EDL) technique are used [39]. In conclusion, the hybrid machine learning model shows promise for heart disease pre-diction, offering improved accuracy and robustness.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Hybrid Classification Model for Heart Disease Prediction

Munsif,

Rashid,

Jabeen

2024

Preprint

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Background: Heart disease prediction is a critical healthcare task for identifying individuals at risk and enabling timely intervention. Accurate prediction models can help reduce morbidity and mortality rates associated with cardiovascular conditions. Various approaches, such as feature selection and hybrid models, have been proposed to improve the effectiveness and accuracy of the prediction of heart disease. Method: The study employs the Genetic Algorithm-Support Vector Machine-Convolutional Neural Network (GA-SVM-CNN) approach and evaluates it on three diverse datasets: UCI, Z-Alizadeh Sani, and Cardiovascular Disease Dataset. The genetic algorithm is utilized first to select the most relevant features from the datasets, effectively reducing dimensionality, eliminating irrelevant or redundant features, and choosing the most suitable ones. Subsequently, the hybrid SVM-CNN model is trained using the selected features, harnessing the complementary capabilities of both techniques to enhance prediction accuracy. Results: The performance of the GA-SVM-CNN approach is assessed using the three benchmark datasets and models. On the UCI dataset, the approach achieves an impressive accuracy of 98%, indicating its effectiveness in accurately predicting heart disease. On the Z-Alizadeh Sani dataset, the approach achieves an accuracy of 97%. On the Cardiovascular Disease Dataset, the approach achieves an accuracy of 86%. These high accuracy rates across different datasets underscore the efficacy of the GA-SVM-CNN approach in heart disease prediction. Conclusion: The combination of the genetic algorithm’s feature selection and the hybrid SVM-CNN model’s predictive power leads to superior performance in heart disease prediction. By accurately identifying individuals at risk of heart disease, this approach can enable timely interventions and contribute to more effective healthcare interventions.

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“…Cardia Magnetic imaging resonance is also utilized to detect abnormalities in coronary arteries of the heart [35]. Following on, some ensemble deep learning-based methods are also employed in the prediction of heart disease [36,37]. Currently, deep learning-based techniques are also heavily used in heart disease predictions.…”

Section: Figure 1: Samples Of Ecg Signals From Mitdb and Ptb Database...mentioning

confidence: 99%

A Smart Heart Disease Diagnostic System Using Deep Vanilla LSTM

Bukhari,

Yasmin,

Naz

et al. 2023

CMC

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Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics, which aid in the prevention of several diseases including heart-related abnormalities. In this context, regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram (ECG) signals has the potential to save many lives. In existing studies, several heart disease diagnostic systems are proposed by employing different state-of-the-art methods, however, improving such methods is always an intriguing area of research. Hence, in this research, a smart healthcare system is proposed for the diagnosis of heart disease using ECG signals. The proposed framework extracts both linear and time-series information on the ECG signals and fuses them into a single framework concurrently. The linear characteristics of ECG signals are extracted by convolution layers followed by Gaussian Error Linear Units (GeLu) and time series characteristics of ECG beats are extracted by Vanilla Long Short-Term Memory Networks (LSTM). Following on, the feature reduction of linear information is done with the help of ID Generalized Gated Pooling (GGP). In addition, data misbalancing issues are also addressed with the help of the Synthetic Minority Oversampling Technique (SMOTE). The performance assessment of the proposed model is done over the two publicly available datasets named MIT-BIH arrhythmia database (MITDB) and PTB Diagnostic ECG database (PTBDB). The proposed framework achieves an average accuracy performance of 99.14% along with a 95% recall value.

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Effective prediction of heart disease using hybrid ensemble deep learning and tunicate swarm algorithm

Cited by 9 publications

References 37 publications

IoT-Enabled Pest Identification and Classification with New Meta-Heuristic-Based Deep Learning Framework

IoT-Enabled Pest Identification and Classification with New Meta-Heuristic-Based Deep Learning Framework

An Efficient Hybrid Classification Model for Heart Disease Prediction

A Smart Heart Disease Diagnostic System Using Deep Vanilla LSTM

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