Araştırma Makalesi

An Interpretable Random Forest Model for Predicting Anosognosia in Preclinical Alzheimer's Disease: Findings from the A4 Study

Cilt: 52 22 Ekim 2026
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An Interpretable Random Forest Model for Predicting Anosognosia in Preclinical Alzheimer's Disease: Findings from the A4 Study

Öz

Anosognosia, reduced awareness of cognitive difficulties, is common across the Alzheimer's disease (AD) continuum yet remains difficult to detect before objective impairment appears; We developed an explainable machine learning model to predict a Cognitive Function Index (CFI) discrepancy–based operational indicator of anosognosia ΔCFI-defined reduced awareness) in amyloid-positive, cognitively unimpaired older adults from the A4 study. Anosognosia was defined as a negative discrepancy between participant- and study-partner-reported CFI scores (ΔCFI<0); the comparison group (ΔCFI>0) reflects hypernosognosia, over-reporting of difficulty relative to the study partner, rather than intact awareness. Participants with ΔCFI=0 or incomplete data were excluded, yielding a final sample of 1,058. A Random Forest classifier was trained on nine features with SHapley Additive exPlanations (SHAP) values computed to quantify predictor contributions at the individual level. The model achieved 65.88% accuracy, 53.45% sensitivity, 70.59% specificity. Hippocampal volume (HV) was the highest predictor by permutation importance, followed by age and study-partner cohabitation status; FCSRT and PACC contributed meaningfully, while MMSE, GDS, and education showed near-zero importance. SHAP analyses confirmed that lower HV, older age, and lower cognitive scores jointly drove predictions of anosognosia, while misclassifications arose from competing demographic and neuroimaging signals. These findings demonstrate that an accessible combination of clinical and neuroimaging variables can identify meaningful predictors of anosognosia in preclinical AD. HV was the leading predictor, consistent with the established role of medial temporal lobe integrity in self-monitoring. The asymmetry among sensitivity, specificity, and positive predictive value underscores the need to incorporate additional biomarkers, particularly tau pathology, functional connectivity, and longitudinal data.

Anahtar Kelimeler

Kaynakça

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  5. 5. López-Martos D, Sánchez-Benavides G, Grau-Rivera O, et al. Clinical and Pathological Progression of Awareness Trajectories in Preclinical Alzheimer's Disease. medRxiv. Feb 18 2026;doi:10.64898/2026.02.16.26346402
  6. 6. Tondelli M, Ballotta D, Maramotti R, et al. Resting-state networks and anosognosia in Alzheimer’s disease. Frontiers in Aging Neuroscience. 2024-06-05 2024;16doi:10.3389/fnagi.2024.1415994
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Ayrıntılar

Birincil Dil

İngilizce

Konular

Nöroloji ve Nöromüsküler Hastalıklar, Sinirbilim (Diğer), Halk Sağlığı (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Yayımlanma Tarihi

22 Ekim 2026

Gönderilme Tarihi

9 Haziran 2026

Kabul Tarihi

8 Ekim 2026

Yayımlandığı Sayı

Yıl 2026 Cilt: 52

DOI

https://doi.org/10.32708/uutfd.1967176

IZ

https://izlik.org/JA74MM73BS

Kaynak Göster

RIS / Bibtex
APA
Demirsoy, I., & Ceren Akgör, M. (2026). An Interpretable Random Forest Model for Predicting Anosognosia in Preclinical Alzheimer’s Disease: Findings from the A4 Study. Journal of Uludağ University Medical Faculty, 52. https://doi.org/10.32708/uutfd.1967176
AMA
1.Demirsoy I, Ceren Akgör M. An Interpretable Random Forest Model for Predicting Anosognosia in Preclinical Alzheimer’s Disease: Findings from the A4 Study. Uludağ Üniv Tıp Fak Derg. 2026;52. doi:10.32708/uutfd.1967176
Chicago
Demirsoy, Idris, ve Merve Ceren Akgör. 2026. “An Interpretable Random Forest Model for Predicting Anosognosia in Preclinical Alzheimer’s Disease: Findings from the A4 Study”. Journal of Uludağ University Medical Faculty 52 (Ekim). https://doi.org/10.32708/uutfd.1967176.
EndNote
Demirsoy I, Ceren Akgör M (01 Ekim 2026) An Interpretable Random Forest Model for Predicting Anosognosia in Preclinical Alzheimer’s Disease: Findings from the A4 Study. Journal of Uludağ University Medical Faculty 52
IEEE
[1]I. Demirsoy ve M. Ceren Akgör, “An Interpretable Random Forest Model for Predicting Anosognosia in Preclinical Alzheimer’s Disease: Findings from the A4 Study”, Uludağ Üniv Tıp Fak Derg, c. 52, Eki. 2026, doi: 10.32708/uutfd.1967176.
ISNAD
Demirsoy, Idris - Ceren Akgör, Merve. “An Interpretable Random Forest Model for Predicting Anosognosia in Preclinical Alzheimer’s Disease: Findings from the A4 Study”. Journal of Uludağ University Medical Faculty 52 (01 Ekim 2026). https://doi.org/10.32708/uutfd.1967176.
JAMA
1.Demirsoy I, Ceren Akgör M. An Interpretable Random Forest Model for Predicting Anosognosia in Preclinical Alzheimer’s Disease: Findings from the A4 Study. Uludağ Üniv Tıp Fak Derg. 2026;52. doi:10.32708/uutfd.1967176.
MLA
Demirsoy, Idris, ve Merve Ceren Akgör. “An Interpretable Random Forest Model for Predicting Anosognosia in Preclinical Alzheimer’s Disease: Findings from the A4 Study”. Journal of Uludağ University Medical Faculty, c. 52, Ekim 2026, doi:10.32708/uutfd.1967176.
Vancouver
1.Idris Demirsoy, Merve Ceren Akgör. An Interpretable Random Forest Model for Predicting Anosognosia in Preclinical Alzheimer’s Disease: Findings from the A4 Study. Uludağ Üniv Tıp Fak Derg. 01 Ekim 2026;52. doi:10.32708/uutfd.1967176

ISSN: 1300-414X, e-ISSN: 2645-9027

Uludağ Üniversitesi Tıp Fakültesi Dergisi "Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License" ile lisanslanmaktadır.


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Journal of Uludag University Medical Faculty is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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