CERAD-NAB and flexible battery based neuropsychological differentiation of Alzheimer’s dementia and depression using machine learning approaches

ABSTRACT

Depression (DEP) and dementia of the Alzheimer’s type (DAT) represent the most common neuropsychiatric disorders in elderly patients. Accurate differential diagnosis is indispensable to ensure appropriate treatment. However, DEP can yet mimic cognitive symptoms of DAT and patients with DAT often also present with depressive symptoms, impeding correct diagnosis. Machine learning (ML) approaches could eventually improve this discrimination using neuropsychological test data, but evidence is still missing. We therefore employed Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest (RF) and conventional Logistic Regression (LR) to retrospectively predict the diagnoses of 189 elderly patients (68 DEP and 121 DAT) based on either the well-established Consortium to Establish a Registry for Alzheimer’s Disease neuropsychological assessment battery (CERAD-NAB) or a flexible battery approach (FLEXBAT). The best performing combination consisted of FLEXBAT and NB, correctly classifying 87.0% of patients as either DAT or DEP. However, all accuracies were similar across algorithms and test batteries (83.0% – 87.0%). Accordingly, our study is the first to show that common ML algorithms with their default parameters can accurately differentiate between patients clinically diagnosed with DAT or DEP using neuropsychological test data, but do not necessarily outperform conventional LR.

KEYWORDS:

• Alzheimer’s dementia
• depression
• neuropsychological assessment
• machine learning
• differential diagnosis
• CERAD-NAB
• flexible battery approach

Acknowledgments

We gratefully acknowledge the work of past and present staff of the Center for Cognitive Disorders and the Clinical and Experimental Neuropsychology Unit at the Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technical University of Munich.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/13825585.2022.2138255

Additional information

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.