Predicting the amount of medical waste using kernel-based SVM and deep learning methods for a private hospital in Turkey
Yazarlar (3)
Dr. Öğr. Üyesi Fatma Gül Altın Burdur Mehmet Akif Ersoy Üniversitesi, Türkiye
Öğr. Gör. Dr. İbrahim BUDAK Kastamonu Üniversitesi, Türkiye
Fatma Özcan Akdeniz Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Sustainable Chemistry and Pharmacy (Q2) | ||
| Dergi ISSN | 2352-5541 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 06-2023 |
| Cilt / Sayı / Sayfa | 33 / 1 / 1–11 | DOI | 10.1016/j.scp.2023.101060 |
| Makale Linki | http://dx.doi.org/10.1016/j.scp.2023.101060 | ||
| UAK Araştırma Alanları |
Karar Destek Sistemleri
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| Özet |
| Successful medical waste management requires accurate forecasting of the amount of waste generation. In the case of increasing the number of independent variables, traditional regression methods are insufficient to predict the amount of waste production. On the other hand, methods such as Kernel-based Support Vector Machine (SVM) and Deep Learning, which have more complex algorithms, give more successful results in predicting the amount of medical waste. In this study, the amount of medical waste for a private hospital in Antalya, Turkey, was predicted using Kernel-based SVM and Deep Learning methods. Epanechnikov function for Kernel-based SVM and Maxout activation function for Deep Learning method were used. The number of surgeries, number of outpatients, number of inpatients, number of intensive care patients and number of intensive care days were determined as the model inputs. In … |
| Anahtar Kelimeler |
| Deep learning | Hospital | Medical waste | SVM |
Science Direct
Predicting the amount of medical waste using kernel-based SVM and deep learning methods for a private hospital in Turkey
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 35 |
| Scopus | 43 |
| Google Scholar | 63 |