Hybrid models based on genetic algorithm and deep learning algorithms for nutritional Anemia disease classification
Yazarlar (3)
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Erciyes Üniversitesi, Türkiye
Doç. Dr. Şafak ŞAHİN
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Biomedical Signal Processing and Control |
| Dergi ISSN | 1746-8094 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2021 |
| Cilt No | 63 |
| Sayı | 102231 |
| DOI Numarası | 10.1016/j.bspc.2020.102231 |
| Makale Linki | https://www.sciencedirect.com/science/article/pii/S174680942030361X |
| Özet |
| Deep learning algorithms are an important part of disease prediction and diagnosis by analyzing health data. If not diagnosed and treated early, symptoms of nutritional anemia can be seen as a common laboratory finding of dyspnea, dizziness, lack of concentration, pale skin color, and life-threatening diseases. In the literature, several data mining techniques have been used for the prediction of nutritional anemia, especially, for the iron deficiency anemia. However, each algorithm does not perform well for every data, and therefore new techniques need to be developed. It is because the characteristics of each dataset are different and their dataset sizes, that is, the number of records and the number of parameters are different. In this study, we propose two hybrid models using genetic algorithm (GA) and deep learning algorithms of Stacked Autoencoder (SAE) and Convolutional Neural Network (CNN) for the … |
| Anahtar Kelimeler |
| Anemia | Classification | Deep learning | SAE | 1D-CNN | Genetic algorithm |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 69 |
| Google Scholar | 132 |