Details

Autor(en) / Beteiligte

• Garcia‐Gutierrez, Fernando
• Delgado‐Alvarez, Alfonso
• Delgado‐Alonso, Cristina
• Díaz‐Álvarez, Josefa
• Pytel, Vanesa
• Valles‐Salgado, Maria
• Gil, María Jose
• Hernández‐Lorenzo, Laura
• Matías‐Guiu, Jorge
• Ayala, José L.
• Matias‐Guiu, Jordi A.

Titel

Diagnosis of Alzheimer's disease and behavioural variant frontotemporal dementia with machine learning‐aided neuropsychological assessment using feature engineering and genetic algorithms

Ist Teil von

• International journal of geriatric psychiatry, 2022-12, Vol.37 (2), p.n/a

Ort / Verlag

England

Erscheinungsjahr

2022

Quelle

Wiley Online Library Journals Frontfile Complete

Beschreibungen/Notizen

• Background Neuropsychological assessment is considered a valid tool in the diagnosis of neurodegenerative disorders. However, there is an important overlap in cognitive profiles between Alzheimer's disease (AD) and behavioural variant frontotemporal dementia (bvFTD), and the usefulness in diagnosis is uncertain. We aimed to develop machine learning‐based models for the diagnosis using cognitive tests. Methods Three hundred and twenty‐nine participants (170 AD, 72 bvFTD, 87 healthy control [HC]) were enrolled. Evolutionary algorithms, inspired by the process of natural selection, were applied for both mono‐objective and multi‐objective classification and feature selection. Classical algorithms (NativeBayes, Support Vector Machines, among others) were also used, and a meta‐model strategy. Results Accuracies for the diagnosis of AD, bvFTD and the differential diagnosis between them were higher than 84%. Algorithms were able to significantly reduce the number of tests and scores needed. Free and Cued Selective Reminding Test, verbal fluency and Addenbrooke's Cognitive Examination were amongst the most meaningful tests. Conclusions Our study found high levels of accuracy for diagnosis using exclusively neuropsychological tests, which supports the usefulness of cognitive assessment in diagnosis. Machine learning may have a role in improving the interpretation and test selection. Key points We developed evolutionary algorithms for an automated diagnosis of behavioural variant frontotemporal dementia (bvFTD) and Alzheimer's disease (AD) based on neuropsychological assessment. Accuracies for the diagnosis of AD, bvFTD and the differential diagnosis between them were higher than 84%. Free and Cued Selective Reminding Test, verbal fluency and Addenbrooke's Cognitive Examination were amongst the most relevant tests for diagnosis. Our study emphasises the role of machine learning in optimising the interpretation of neuropsychological assessment, test selection and cut‐off point determination.