Efekan Kavalcı
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Efekan Kavalcı has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Ayşe Rana Övüt
Hatay Mustafa Kemal Üniversitesi
1 shared publication - Başak Ceren Okcu Korkmaz
Department of Neurology, University of Health Sciences Sultan Abdulhamid Han Research and Training Hospital, Istanbul 34668, Turkey
1 shared publication - Buse Gül Belen
Department of Neurology, University of Health Sciences Sultan Abdulhamid Han Research and Training Hospital, Istanbul 34668, Turkey
1 shared publication - Elvan Karasu
Department of Computer Engineering, Boğaziçi University, Istanbul 34342, Turkey
1 shared publication - Hakan Mutlu
Department of Radiology, University of Health Sciences Sultan Abdulhamid Han Research and Training Hospital, Istanbul 34668, Turkey
1 shared publication - Mehmet Fatih Özdağ
Department of Neurology, University of Health Sciences Sultan Abdulhamid Han Research and Training Hospital, Istanbul 34668, Turkey
1 shared publication
Latest Publications
- Open Access
- 16 March 2026
- Published by MDPI AG in Journal of Clinical Medicine
- Vol. 15 (6) , 2250
Real-World Multimodal Machine Learning for Risk Enrichment Across the Alzheimer’s Disease Spectrum
Background and Objectives: Mild cognitive impairment (MCI) is heterogeneous within the Alzheimer’s disease (AD) continuum, and categorical labels may not reflect biological variability. We evaluated whether multimodal machine learning using routine clinical data and neuroimaging could support biologically informed enrichment across MCI and AD in a real-world memory clinic cohort. Methods: We analyzed 474 patients (1547 visits) with clinical and cognitive measures, laboratory parameters, MRI regional volumes, and FDG-PET regional uptake. Elastic Net and gradient boosting models were trained using nested cross-validation with strict patient-level separation. Results: Model discrimination improved as additional data modalities were added, and FDG-PET contributed the largest performance improvement. Hypometabolism in posterior default mode network regions consistently emerged as the most influential predictor. In the MCI subgroup, AD-like scores showed a continuous distribution consistent with biological enrichment. Conclusions: Multimodal models may provide an interpretable enrichment framework in heterogeneous memory clinic populations.
- 16 March 2026
- Published by MDPI AG in Journal of Clinical Medicine
- Vol. 15 (6) , 2250
Open AccessReal-World Multimodal Machine Learning for Risk Enrichment Across the Alzheimer’s Disease Spectrum
Background and Objectives: Mild cognitive impairment (MCI) is heterogeneous within the Alzheimer’s disease (AD) continuum, and categorical labels may not reflect biological variability. We evaluated whether multimodal machine learning using routine clinical data and neuroimaging could support biologically informed enrichment across MCI and AD in a real-world memory clinic cohort. Methods: We analyzed 474 patients (1547 visits) with clinical and cognitive measures, laboratory parameters, MRI regional volumes, and FDG-PET regional uptake. Elastic Net and gradient boosting models were trained using nested cross-validation with strict patient-level separation. Results: Model discrimination improved as additional data modalities were added, and FDG-PET contributed the largest performance improvement. Hypometabolism in posterior default mode network regions consistently emerged as the most influential predictor. In the MCI subgroup, AD-like scores showed a continuous distribution consistent with biological enrichment. Conclusions: Multimodal models may provide an interpretable enrichment framework in heterogeneous memory clinic populations.