| Yazarlar |
Doç. Dr. Kemal AKYOL
Kastamonu Üniversitesi, Türkiye |
| Özet |
| Autistic Spectrum Disorder (ASD) is a cognitive disease which leads to the loss of linguistic, communicative, cognitive, and social skills and abilities. Patients with ASD have diverse troubles such as sleeping problems. The role of genetic and environmental factors is of great importance in its pathophysiology. Early diagnosis provides an improved overall mental health of the patients. This study aimed to identify the important attributes for the best detection of this disorder in children, adolescents and adults. To achieve this aim, Recursive Feature Elimination and Stability Selection methods that consider important attributes for target class were proposed. The attributes collected from autism screening methods and other attributes such as age and gender were examined for the disease. The results verified the combining of feature selection method and machine learning algorithm within the frame of accuracy, sensitivity and specificity evaluation metrics. |
| Anahtar Kelimeler |
| autistic spectrum disorder | importance of autistic attributes | machine learning | recursive feature elimination | stability selection |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | EXPERT SYSTEMS |
| Dergi ISSN | 0266-4720 |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2020 |
| Cilt No | 37 |
| Sayı | 5 |
| Sayfalar | 1 / 10 |
| Doi Numarası | 10.1111/exsy.12562 |
| Makale Linki | https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.12562 |
| Atıf Sayıları | |
| SCOPUS | 5 |
| Google Scholar | 8 |
