Machine learning–based identification of multidimensional predictors of quality of life in individuals with multiple sclerosis
Yazarlar (7)
Dr. Öğr. Üyesi Aysu YETİŞ Kırşehir Ahi Evran Üniversitesi, Türkiye
Dr. Öğr. Üyesi Mehmet CANLI Kırşehir Ahi Evran Üniversitesi, Türkiye
Arş. Gör. İrem CANLI Kırşehir Ahi Evran Üniversitesi, Türkiye
Hikmet Kocaman
Hasan Yildirim
Nazim Tolgahan Yildiz
Doç. Dr. Selcen DURAN Kırşehir Ahi Evran Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | MEDICINE (Q2) | ||
| Dergi ISSN | 0025-7974 Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 05-2026 |
| Cilt / Sayı / Sayfa | 105 / 22 / 22– | DOI | 10.1097/MD.0000000000049025 |
| Makale Linki | https://doi.org/10.1097/md.0000000000049025 | ||
| UAK Araştırma Alanları |
Fizyoterapi ve Rehabilitasyon
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| Özet |
| This study aimed to identify independent predictors of quality of life in patients with multiple sclerosis (MS) using machine learning approaches. One hundred and one individuals diagnosed with MS were included in this cross-sectional study. Quality of life was assessed using the MS Quality of Life-54 (MSQoL-54). Demographic variables, clinical characteristics, disability level (Expanded Disability Status Scale [EDSS]), fatigue severity, sleep quality (Pittsburgh Sleep Quality Index), depression level (Beck Depression Inventory), and functional mobility (Timed Up and Go test) were evaluated. Multiple machine learning regression models, including Linear Regression, Lasso, Elastic Net, Support Vector Machines, Random Forest, and XGBoost, were developed and compared. Five-times repeated five-fold cross-validation was applied for internal validation. Model performance was evaluated using root mean squared … |
| Anahtar Kelimeler |
| disability | machine learning | multiple sclerosis | quality of life |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 1 |
| Google Scholar | 2 |