Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle
People with underactive thyroids frequently endure severe symptoms. Correct classification and machine learning substantially improve thyroid disease diagnosis. This precise classification will impact the timely delivery of care to the patients. Although diagnostic techniques exist, they frequently...
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| Language: | English |
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University of Baghdad, College of Science for Women
2024-05-01
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| Series: | مجلة بغداد للعلوم |
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| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/10547 |
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| author | Rachappa Jopate Piyush Kumar Pareek DivyaJyothi M. G Ariam Saleh Zuwayid Juma Al Hasani |
| author_facet | Rachappa Jopate Piyush Kumar Pareek DivyaJyothi M. G Ariam Saleh Zuwayid Juma Al Hasani |
| author_sort | Rachappa Jopate |
| collection | DOAJ |
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People with underactive thyroids frequently endure severe symptoms. Correct classification and machine learning substantially improve thyroid disease diagnosis. This precise classification will impact the timely delivery of care to the patients. Although diagnostic techniques exist, they frequently seek binary categorization, use insufficiently big datasets, and lack confirmation of their conclusions. The focus of current approaches is on model optimisation, whereas feature engineering is neglected. This research presents the Adaptive Elephant Herd Optimisation Algorithm (AEHOA) model for selecting optimal attributes in order to circumvent these limitations. At first, employ a method called the Synthetic Minority Over-sampling Technique (SMOTE) to even out the data. Finally, the parameters of the AEHOA model are fed into a Convolutional Neural Network (CNN) to categorise data and enhance prediction. The accuracy of classification predictions was also increased by tweaking the dataset. Both datasets were put through a categorization process for a more precise comparison of results.
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| format | Article |
| id | doaj-art-9f22d87cd5c54ba382090f2f4f69bda2 |
| institution | Kabale University |
| issn | 2078-8665 2411-7986 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | University of Baghdad, College of Science for Women |
| record_format | Article |
| series | مجلة بغداد للعلوم |
| spelling | doaj-art-9f22d87cd5c54ba382090f2f4f69bda22025-08-20T03:35:48ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862024-05-01215(SI)10.21123/bsj.2024.10547Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy LifestyleRachappa Jopate0https://orcid.org/0009-0007-4875-5719Piyush Kumar Pareek1DivyaJyothi M. G 2Ariam Saleh Zuwayid Juma Al Hasani3Department of Information Technology, University of Technology and Applied Sciences, Al Mussanah, Oman.Department of AI ML, NITTE Meenakshi Institute of Technology, Bangalore, India.Department of Information Technology, University of Technology and Applied Sciences, Al Mussanah, Oman.Department of Information Technology, University of Technology and Applied Sciences, Al Mussanah, Oman. People with underactive thyroids frequently endure severe symptoms. Correct classification and machine learning substantially improve thyroid disease diagnosis. This precise classification will impact the timely delivery of care to the patients. Although diagnostic techniques exist, they frequently seek binary categorization, use insufficiently big datasets, and lack confirmation of their conclusions. The focus of current approaches is on model optimisation, whereas feature engineering is neglected. This research presents the Adaptive Elephant Herd Optimisation Algorithm (AEHOA) model for selecting optimal attributes in order to circumvent these limitations. At first, employ a method called the Synthetic Minority Over-sampling Technique (SMOTE) to even out the data. Finally, the parameters of the AEHOA model are fed into a Convolutional Neural Network (CNN) to categorise data and enhance prediction. The accuracy of classification predictions was also increased by tweaking the dataset. Both datasets were put through a categorization process for a more precise comparison of results. https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/10547Adaptive Elephant Herd Optimization Algorithm, Convolutional Neural Network, Hyperthyroidism Imbalanced data, Machine Learning, Synthetic Minority Over-sampling Technique |
| spellingShingle | Rachappa Jopate Piyush Kumar Pareek DivyaJyothi M. G Ariam Saleh Zuwayid Juma Al Hasani Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle مجلة بغداد للعلوم Adaptive Elephant Herd Optimization Algorithm, Convolutional Neural Network, Hyperthyroidism Imbalanced data, Machine Learning, Synthetic Minority Over-sampling Technique |
| title | Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle |
| title_full | Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle |
| title_fullStr | Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle |
| title_full_unstemmed | Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle |
| title_short | Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle |
| title_sort | prediction of thyroid classes using feature selection of aehoa based cnn model for healthy lifestyle |
| topic | Adaptive Elephant Herd Optimization Algorithm, Convolutional Neural Network, Hyperthyroidism Imbalanced data, Machine Learning, Synthetic Minority Over-sampling Technique |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/10547 |
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