Super Learner Algorithm for Carotid Artery Disease Diagnosis: A Machine Learning Approach Leveraging Craniocervical CT Angiography
Makaleler > Tam Makale · 2024
Yazarlar: Halil İbrahim Özdemir, Kazım Gökhan Atman, Hüseyin ŞİRİN, Abdullah Engin ÇALIK, İbrahim ŞENTÜRK, Metin Bilge, İsmail Oran, Melek Duygu BİLGE, Celal Çınar
TOMOGRAPHY, SCI Expanded İndekslerine Giren Dergi, Uluslararası, Wos, 2024, cilt 10, ss 1622-1644
DOI: 10.3390/tomography10100120
Özet
This study introduces a machine learning (ML) approach to diagnosing carotid artery diseases, including stenosis, aneurysm, and dissection, by leveraging craniocervical computed tomography angiography (CTA) data. A meticulously curated, balanced dataset of 122 patient cases was used, ensuring reproducibility and data quality, and this is publicly accessible at (insert dataset location). The proposed method integrates a super learner model which combines adaptive boosting, gradient boosting, and random forests algorithms, achieving an accuracy of (Formula presented.). To enhance model robustness and generalization, techniques such as k-fold cross-validation, bootstrapping, data augmentation, and the synthetic minority oversampling technique (SMOTE) were applied, expanding the dataset to 1000 instances and significantly improving performance for minority classes like aneurysm and dissection. The results highlight the pivotal role of blood vessel structural analysis in diagnosing carotid artery diseases and demonstrate the superior performance of the super learner model in comparison with state-of-the-art (SOTA) methods in terms of both accuracy and robustness. This manuscript outlines the methodology, compares the results with state-of-the-art approaches, and provides insights for future research directions in applying machine learning to medical diagnostics.