Thyroid Disease Multi-class Classification based on Optimized Gradient Boosting Model
Alnaggar, M., Handosa, M., Medhat, T., Z. Rashad, M. (2023). Thyroid Disease Multi-class Classification based on Optimized Gradient Boosting Model. EKB Journal Management System, 2(1), 1-14. doi: 10.21608/ejai.2023.205554.1008
Mona Alnaggar; Mohamed Handosa; Tamer Medhat; M. Z. Rashad. "Thyroid Disease Multi-class Classification based on Optimized Gradient Boosting Model". EKB Journal Management System, 2, 1, 2023, 1-14. doi: 10.21608/ejai.2023.205554.1008
Alnaggar, M., Handosa, M., Medhat, T., Z. Rashad, M. (2023). 'Thyroid Disease Multi-class Classification based on Optimized Gradient Boosting Model', EKB Journal Management System, 2(1), pp. 1-14. doi: 10.21608/ejai.2023.205554.1008
Alnaggar, M., Handosa, M., Medhat, T., Z. Rashad, M. Thyroid Disease Multi-class Classification based on Optimized Gradient Boosting Model. EKB Journal Management System, 2023; 2(1): 1-14. doi: 10.21608/ejai.2023.205554.1008

Thyroid Disease Multi-class Classification based on Optimized Gradient Boosting Model

Egyptian Journal of Artificial Intelligence
Volume 2, Issue 1, April 2023, Page 1-14  XML PDF (879.82 K)
Document Type: Original Article
DOI: 10.21608/ejai.2023.205554.1008
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Authors
Mona Alnaggar email orcid 1; Mohamed Handosa2; Tamer Medhatorcid 3; M. Z. Rashad4
1Robotics and intelligent machines, faculty of artificial intelligence, Kafrelsheikh University
2Department of Computer science, Faculty of Computers and Information, Mansoura University, Egypt
3Electrical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Egypt
4Computer science Department, Faculty of Computers and Information, Mansoura University, Egypt
Abstract
Human healthcare is one of the most important issues in society to ensure that patients receive the care they require as quickly as possible. One of the disorders that affects the global population and is becoming more prevalent is thyroid disease. Medical information systems are crucial in their capacity to diagnose thyroid disease. Artificial intelligence has recently offered fresh approaches to the existing clinical treatment issues and has demonstrated promising results for individualized diagnosis and therapy planning. Hence, this paper proposes an optimized multi-class classification model, which depends on XGBoost to classify patients with different types of thyroid disease. The main contribution is to (i) propose a Multiclass-Classification for the purpose of diagnosing three different thyroid diseases, (ii) raise the row dataset's feature selection accuracy for classification. (iii) utilize the highly selective XGBoost algorithm for the chosen characteristics, (iv) show that The XGBoost has the best performance and recall, making it the top choice for data analysis in terms of classifying thyroid disease, and (v) improve upon findings from earlier studies by doing the proposed study. XGBoost is trained and tested using UCI machine learning repository dataset of thyroid disease. In addition to build the model with the optimized hyperparameters to achieve and compare the gained results aiming to get the best score of accuracy. From the results, it is shown that the optimized XGBoost achieved 99% accuracy as a win over performing compared with the state of arts models.
Keywords
Human healthcare; Thyroid disease; Artificial intelligence; XGBoost algorithm; hyperparameters optimization
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