Affiliations: [a] Instituto de Psicología, Universidad del Valle, Santiago de Cali, Colombia
| [b] Departamento de Estudios Psicológicos, Facultad de Derecho y Ciencias Sociales, Universidad ICESI , Santiago de Cali, Colombia
| [c] Departamento de Psiquiatría, Facultad de Salud, Universidad del Valle, Santiago de Cali, Colombia
| [d]
Hospital Departamental Psiquiátrico Universitario del Valle, Santiago de Cali, Colombia. | [e] Departamento de Psiquiatría, Fundación Valle del Lili, Santiago de Cali, Colombia
| [f] Departamento de Imágenes Diagnósticas, Fundación Valle del Lili, Santiago de Cali, Colombia
| [g] Universidad Santiago de Cali, Santiago de Cali, Colombia
Correspondence:
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Correspondence to: Juan F. Cardona, Universidad del Valle, Ciudad Universitaria Meléndez, Calle 13 No. 100-00, Edificio 388 Oficina 4035, Santiago de Cali, Colombia. Tel.: +57 2 321 21 00 Ext. 2579; E-mail: [email protected].
Abstract: Background:Amnestic mild cognitive impairment (aMCI) is the most common preclinical stage of Alzheimer’s disease (AD). A strategy to reduce the impact of AD is the early aMCI diagnosis and clinical intervention. Neuroimaging, neurobiological, and genetic markers have proved to be sensitive and specific for the early diagnosis of AD. However, the high cost of these procedures is prohibitive in low-income and middle-income countries (LIMCs). The neuropsychological assessments currently aim to identify cognitive markers that could contribute to the early diagnosis of dementia. Objective:Compare machine learning (ML) architectures classifying and predicting aMCI and asset the contribution of cognitive measures including binding function in distinction and prediction of aMCI. Methods:We conducted a two-year follow-up assessment of a sample of 154 subjects with a comprehensive multidomain neuropsychological battery. Statistical analysis was proposed using complete ML architectures to compare subjects’ performance to classify and predict aMCI. Additionally, permutation importance and Shapley additive explanations (SHAP) routines were implemented for feature importance selection. Results:AdaBoost, gradient boosting, and XGBoost had the highest performance with over 80%success classifying aMCI, and decision tree and random forest had the highest performance with over 70%success predictive routines. Feature importance points, the auditory verbal learning test, short-term memory binding tasks, and verbal and category fluency tasks were used as variables with the first grade of importance to distinguish healthy cognition and aMCI. Conclusion:Although neuropsychological measures do not replace biomarkers’ utility, it is a relatively sensitive and specific diagnostic tool for aMCI. Further studies with ML must identify cognitive performance that differentiates conversion from average MCI to the pathological MCI observed in AD.
