Assessing the importance of autistic attributes for autism screening
Yazarlar (1)
Prof. Dr. Kemal AKYOL Kastamonu Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Expert Systems | ||
| Dergi ISSN | 0266-4720 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2020 |
| Kabul Tarihi | 01-04-2020 | Yayınlanma Tarihi | 17-04-2020 |
| Cilt / Sayı / Sayfa | 37 / 5 / 1–10 | DOI | 10.1111/exsy.12562 |
| Makale Linki | https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.12562 | ||
| 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 … |
| Anahtar Kelimeler |
| autistic spectrum disorder | importance of autistic attributes | machine learning | recursive feature elimination | stability selection |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 8 |
| Google Scholar | 13 |