The Efficiency of Ensemble Techniques in Predicting Thyroid Disorder: A Comparative Study
Yazarlar (2)
Muntadher Alsaadawı Karabük Üniversitesi, Türkiye
Doç. Dr. Eftal ŞEHİRLİ Karabük Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.1109/ISMSIT56059.2022.9932774 | ||
| Kongre Adı | 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) | ||
| Kongre Tarihi | 20-10-2022 / 22-10-2022 | ||
| Basıldığı Ülke | Türkiye | Basıldığı Şehir | Ankara |
| Bildiri Linki | http://dx.doi.org/10.1109/ismsit56059.2022.9932774 | ||
| UAK Araştırma Alanları |
Makine Öğrenmesi
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| Özet |
| 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 … |
| Anahtar Kelimeler |
| accuracy | ensemble | feature selection | machine learning | RFE | SMOTE | stacking | thyroid disease |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 2 |
| Google Scholar | 2 |