Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle
Abstract
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.
Keywords
Adaptive Elephant Herd Optimization Algorithm, Convolutional Neural Network, Hyperthyroidism Imbalanced data, Machine Learning, Synthetic Minority Over-sampling Technique
Article Type
Special Issue Article
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite this Article
Jopate, Rachappa; Pareek, Piyush Kumar; G, DivyaJyothi M.; and Al Hasani, Ariam Saleh Zuwayid Juma
(2024)
"Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle,"
Baghdad Science Journal: Vol. 21:
Iss.
5, Article 29.
DOI: https://doi.org/10.21123/bsj.2024.10547