Affiliations: [a] Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain
| [b] Memory Unit, Department of Neurology, Clínica Universidad de Navarra, Pamplona, Spain
| [c] IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
| [d] Institute of Data Science and Artificial Intelligence, Universidad de Navarra, Pamplona, Spain
Correspondence:
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Correspondence to: María A. Fernández Seara, Department of Radiology, Clínica Universidad de Navarra, Avda de Pio XII, 36, 31008, Pamplona, Spain. E-mail: [email protected].
Note: [1] These authors contributed equally to this work.
Abstract: Background:Subjective cognitive decline (SCD) may represent a preclinical stage of Alzheimer’s disease (AD). Predicting progression of SCD patients is of great importance in AD-related research but remains a challenge. Objective:To develop and implement an ensemble machine learning (ML) algorithm to identify SCD subjects at risk of conversion to mild cognitive impairment (MCI) or AD. Methods:Ninety-nine SCD patients were included. Thirty-two progressed to MCI/AD, while 67 remained stable. To minimize the effect of class imbalance, both classes were balanced, and sensitivity was taken as evaluation metric. Bagging and boosting ML models were developed by using socio-demographic and clinical information, Mini-Mental State Examination and Geriatric Depression Scale (GDS) scores (feature-set 1a); socio-demographic characteristics and neuropsychological tests scores (feature-set 1b) and regional magnetic resonance imaging grey matter volumes (feature-set 2). The most relevant variables were combined to find the best model. Results:Good prediction performances were obtained with feature-sets 1a and 2. The most relevant variables (variable importance exceeding 20%) were: Age, GDS, and grey matter volumes measured in four cortical regions of interests. Their combination provided the optimal classification performance (highest sensitivity and specificity) ensemble ML model, Extreme Gradient Boosting with over-sampling of the minority class, with performance metrics: sensitivity = 1.00, specificity = 0.92 and area-under-the-curve = 0.96. The median values based on fifty random train/test splits were sensitivity = 0.83 (interquartile range (IQR) = 0.17), specificity = 0.77 (IQR = 0.23) and area-under-the-curve = 0.75 (IQR = 0.11). Conclusion:A high-performance algorithm that could be translatable into practice was able to predict SCD conversion to MCI/AD by using only six predictive variables.
