A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients
COVID-19, one of the most dangerous pandemics, is currently affecting humanity. COVID-19 is spreading rapidly due to its high reliability transmissibility. Patients who test positive more often have mild to severe symptoms such as a cough, fever, raw throat, and muscle aches. Diseased people experie...
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| Format: | Article |
| Language: | English |
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Sulaimani Polytechnic University
2021-12-01
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| Series: | Kurdistan Journal of Applied Research |
| Subjects: | |
| Online Access: | https://kjar.spu.edu.iq/index.php/kjar/article/view/702 |
| _version_ | 1797197694022713344 |
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| author | Alla Ahmad Hassan Tarik A Rashid |
| author_facet | Alla Ahmad Hassan Tarik A Rashid |
| author_sort | Alla Ahmad Hassan |
| collection | DOAJ |
| description | COVID-19, one of the most dangerous pandemics, is currently affecting humanity. COVID-19 is spreading rapidly due to its high reliability transmissibility. Patients who test positive more often have mild to severe symptoms such as a cough, fever, raw throat, and muscle aches. Diseased people experience severe symptoms in more severe cases. such as shortness of breath, which can lead to respiratory failure and death. Machine learning techniques for detection and classification are commonly used in current medical diagnoses. However, for treatment using neural networks based on improved Particle Swarm Optimization (PSO), known as PSONN, the accuracy and performance of current models must be improved. This hybridization implements Particle Swarm Optimization and a neural network to improve results while slowing convergence and improving efficiency. The purpose of this study is to contribute to resolving this issue by presenting the implementation and assessment of Machine Learning models. Using Neural Networks and Particle Swarm Optimization to help in the detection of COVID-19 in its early stages. To begin, we preprocessed data from a Brazilian dataset consisted primarily of early-stage symptoms. Following that, we implemented Neural Network and Particle Swarm Optimization algorithms. We used precision, accuracy score, recall, and F-Measure tests to evaluate the Neural Network with Particle Swarm Optimization algorithms. Based on the comparison, this paper grouped the top seven ML models such as Neural Networks, Logistic Regression, Nave Bayes Classifier, Multilayer Perceptron, Support Vector Machine, BF Tree, Bayesian Networks algorithms and measured feature importance, and other, to justify the differences between classification models. Particle Swarm Optimization with Neural Network is being deployed to improve the efficiency of the detection method by more accurately predicting COVID-19 detection. Preprocessed datasets with important features are then fed into the testing and training phases as inputs. Particle Swarm Optimization was used for the training phase of a neural net to identify the best weights and biases. On training data, the highest rate of accuracy gained is 0.98.738 and on testing data, it is 98.689.
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| first_indexed | 2024-03-07T14:11:04Z |
| format | Article |
| id | doaj.art-c3a07b5739ab415da9d5126f1621f361 |
| institution | Directory Open Access Journal |
| issn | 2411-7684 2411-7706 |
| language | English |
| last_indexed | 2024-04-24T06:48:02Z |
| publishDate | 2021-12-01 |
| publisher | Sulaimani Polytechnic University |
| record_format | Article |
| series | Kurdistan Journal of Applied Research |
| spelling | doaj.art-c3a07b5739ab415da9d5126f1621f3612024-04-22T17:18:55ZengSulaimani Polytechnic UniversityKurdistan Journal of Applied Research2411-76842411-77062021-12-016210.24017/science.2021.2.5702A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 PatientsAlla Ahmad Hassan0Tarik A RashidSulaimani Polytechnic UniversityCOVID-19, one of the most dangerous pandemics, is currently affecting humanity. COVID-19 is spreading rapidly due to its high reliability transmissibility. Patients who test positive more often have mild to severe symptoms such as a cough, fever, raw throat, and muscle aches. Diseased people experience severe symptoms in more severe cases. such as shortness of breath, which can lead to respiratory failure and death. Machine learning techniques for detection and classification are commonly used in current medical diagnoses. However, for treatment using neural networks based on improved Particle Swarm Optimization (PSO), known as PSONN, the accuracy and performance of current models must be improved. This hybridization implements Particle Swarm Optimization and a neural network to improve results while slowing convergence and improving efficiency. The purpose of this study is to contribute to resolving this issue by presenting the implementation and assessment of Machine Learning models. Using Neural Networks and Particle Swarm Optimization to help in the detection of COVID-19 in its early stages. To begin, we preprocessed data from a Brazilian dataset consisted primarily of early-stage symptoms. Following that, we implemented Neural Network and Particle Swarm Optimization algorithms. We used precision, accuracy score, recall, and F-Measure tests to evaluate the Neural Network with Particle Swarm Optimization algorithms. Based on the comparison, this paper grouped the top seven ML models such as Neural Networks, Logistic Regression, Nave Bayes Classifier, Multilayer Perceptron, Support Vector Machine, BF Tree, Bayesian Networks algorithms and measured feature importance, and other, to justify the differences between classification models. Particle Swarm Optimization with Neural Network is being deployed to improve the efficiency of the detection method by more accurately predicting COVID-19 detection. Preprocessed datasets with important features are then fed into the testing and training phases as inputs. Particle Swarm Optimization was used for the training phase of a neural net to identify the best weights and biases. On training data, the highest rate of accuracy gained is 0.98.738 and on testing data, it is 98.689. https://kjar.spu.edu.iq/index.php/kjar/article/view/702Particle Swarm Optimization, Neural Networks, Logistic Regression, Nave Bayes Classifier, Multilayer Perceptron, Support Vector Machine, BF Tree, Bayesian Networks. |
| spellingShingle | Alla Ahmad Hassan Tarik A Rashid A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients Kurdistan Journal of Applied Research Particle Swarm Optimization, Neural Networks, Logistic Regression, Nave Bayes Classifier, Multilayer Perceptron, Support Vector Machine, BF Tree, Bayesian Networks. |
| title | A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients |
| title_full | A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients |
| title_fullStr | A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients |
| title_full_unstemmed | A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients |
| title_short | A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients |
| title_sort | hybrid artificial neural network and particle swarm optimization algorithm for detecting covid 19 patients |
| topic | Particle Swarm Optimization, Neural Networks, Logistic Regression, Nave Bayes Classifier, Multilayer Perceptron, Support Vector Machine, BF Tree, Bayesian Networks. |
| url | https://kjar.spu.edu.iq/index.php/kjar/article/view/702 |
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