The Efficiency of Ensemble Techniques in Predicting Thyroid Disorder: A Comparative Study

• Main Authors: Alsaadawi, Muntadher, Sehirli, Eftal
• Format: Conference Proceeding
• Language: English
• Subjects: accuracy; Classification algorithms; Computational modeling; ensemble; Feature extraction; feature selection; machine learning; Prediction algorithms; Predictive models; RFE; SMOTE; Stacking; thyroid disease; Training
• ISSN: 2770-7962
• DOI: 10.1109/ISMSIT56059.2022.9932774

Data science is presently connected with a wide range of technical and scientific fields. Thyroid disorder is a widespread issue that affects a great variety of people. Hospitals report several forms of thyroid conditions. In this thesis, a thyroid disease prediction model has been created by classification and comparing traditional and Ensemble algorithms. A dataset including 1,250 records from the Iraqi people was utilized for the first-time using Ensemble methods. Stacking is one of the most effective Ensemble approaches for forecasting complicated structured data. Several metrics, including Accuracy, Precision, Sensitivity, Specificity, F-Score, and the Matthews correlation coefficient, were used to evaluate the performance of the prediction model. The experimental findings show that the proposed technique to optimize the detection of thyroid illnesses may be successfully implemented. The majority of Ensemble methods achieved 100 % accuracy with both the whole data set and the feature selection data set. In terms of precision and computational expense, the given findings outperform comparable models in their field.