A predictive study on HCV using automated machine learning models
Yazarlar (2)
Dr. Öğr. Üyesi Serbun Ufuk DEĞER Kastamonu Üniversitesi, Türkiye
Öğr. Gör. Hakan CAN Kastamonu Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Computers in Biology and Medicine (Q1) | ||
| Dergi ISSN | 0010-4825 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 04-2025 |
| Cilt / Sayı / Sayfa | 188 / 1 / – | DOI | 10.1016/j.compbiomed.2025.109897 |
| Makale Linki | https://doi.org/10.1016/j.compbiomed.2025.109897 | ||
| Özet |
| Hepatitis C virus (HCV) infection represents a significant contributor to chronic liver disease on a global scale. The prompt identification and management of HCV are imperative in order to avert complications and to maintain control over the disease. Nowadays, medical decision support systems that incorporate advanced diagnostic methods and effective treatment strategies are of great importance in order to make significant progress in the fight against HCV. Medical decision support systems have undergone a major evolution with the development of computer technologies. In the 2010s, the integration of big data and artificial intelligence technologies into medical decision support systems enabled rapid analysis of patient data. This has created significant synergies in the diagnostic and therapeutic approaches to various diseases. The ever-increasing volume of data on HCV infection offers opportunities to use … |
| Anahtar Kelimeler |
| Auto machine learning | Data science | Hepatitis C | Visualization |
Science Direct
A predictive study on HCV using automated machine learning models
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 3 |
| Google Scholar | 5 |