| Yazarlar (1) |
Dr. Öğr. Üyesi Kağan TUR
Kırşehir Ahi Evran Üniversitesi, Türkiye |
| Özet |
| : Accurate and rapid detection of COVID-19 remains critical for clinical management, especially in resource-limited settings. Current diagnostic methods face challenges in terms of speed and reliability, creating a need for complementary AI-based models that integrate diverse data sources. : This study aimed to develop and evaluate a multi-modal machine learning model that combines clinical biomarkers and chest X-ray images to enhance diagnostic accuracy and provide interpretable insights. : We used a dataset of 250 patients (180 COVID-19 positive and 70 negative cases) collected from clinical settings. Biomarkers such as CRP, ferritin, NLR, and albumin were included alongside chest X-ray images. Random Forest and Gradient Boosting models were used for biomarkers, and ResNet and VGG CNN architectures were applied to imaging data. A late-fusion strategy integrated predictions from these modalities. Stratified k-fold cross-validation ensured robust evaluation while preventing data leakage. Model performance was assessed using AUC-ROC, F1-score, Specificity, Negative Predictive Value (NPV), and Matthews Correlation Coefficient (MCC), with confidence intervals calculated via bootstrap resampling. : The Gradient Boosting + VGG fusion model achieved the highest performance, with an AUC-ROC of 0.94, F1-score of 0.93, Specificity of 93%, NPV of 96%, and MCC of 0.91. SHAP and LIME interpretability analyses identified CRP, ferritin, and specific lung regions as key contributors to predictions. : The proposed multi-modal approach significantly enhances diagnostic accuracy compared to single-modality models. Its interpretability aligns with clinical understanding, supporting its potential for real-world application. |
| Anahtar Kelimeler |
| multi-modal machine learning | COVID-19 diagnostics | biomarkers and imaging | SHAP and LIME analysis |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Diagnostics |
| Dergi ISSN | 2075-4418 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q1 |
| Makale Dili | İngilizce |
| Basım Tarihi | 12-2024 |
| Cilt No | 14 |
| Sayı | 24 |
| Doi Numarası | 10.3390/diagnostics14242800 |
| Makale Linki | https://doi.org/10.3390/diagnostics14242800 |
| Atıf Sayıları | |
| WoS | 2 |
