| Yazarlar (1) |
Dr. Öğr. Üyesi Serbun Ufuk DEĞER
Kastamonu Üniversitesi, Türkiye |
| Özet |
| Cardiovascular diseases comprise a diverse array of disorders impacting the cardiac structure and vascular system and rank among the predominant factors contributing to mortality on a global scale. Every day, a significant number of individuals die from various heart-related issues. Therefore, early detection of heart diseases is of critical importance. Especially following these diagnoses, providing a more accurate diagnosis for individuals at high risk and subsequent extra treatments outline an essential roadmap for preventing heart attacks. This paper compares the performance of classic machine learning (ClassicML) and automated machine learning (AutoML) models across different variations that incorporate feature engineering and balancing techniques, thereby identifying which machine learning model is more successful in the development and implementation of patient-centered systems for the early ... |
| Anahtar Kelimeler |
| AutoML | ClassicML | Heart disease prediction | Machine learning | Partial derivative | Visualization |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | Uluslararası alan indekslerindeki dergilerde yayımlanan tam makale |
| Dergi Adı | Computers in Biology and Medicine |
| Dergi ISSN | 0010-4825 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | |
| Makale Dili | İngilizce |
| Basım Tarihi | 06-2025 |
| Cilt No | 192 |
| Sayı | 1 |
| Sayfalar | 110308 / 0 |
| Doi Numarası | 10.1016/j.compbiomed.2025.110308 |
| Atıf Sayıları |
