A multi-expert ensemble system for predicting Alzheimer transition using clinical features

• Published in: Brain informatics Vol. 9; no. 1; pp. 20 - 11
• Main Authors: Merone, Mario, D’Addario, Sebastian Luca, Mirino, Pierandrea, Bertino, Francesca, Guariglia, Cecilia, Ventura, Rossella, Capirchio, Adriano, Baldassarre, Gianluca, Silvetti, Massimo, Caligiore, Daniele
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2022; Springer; Springer Nature B.V; SpringerOpen
• Subjects: ADAS score; Advertising executives; Algorithms; Alzheimer's disease; Artificial Intelligence; Brain damage; Cerebellar impairment; Clinical Dementia Rating Scale; Cognitive Psychology; Computation by Abstract Devices; Computational Intelligence for Brain and Mental Health Research; Computer Science; Data mining; Diagnosis; Early diagnosis; Health Informatics; Machine learning; Neurosciences; Renal and genitourinary dysfunctions; Signs and symptoms; Clinical Dementia Rating Scale; Renal and genitourinary dysfunctions; Early diagnosis; ADAS score; Cerebellar impairment; Machine learning
• ISSN: 2198-4018; 2198-4026; 2198-4026
• DOI: 10.1186/s40708-022-00168-2

Alzheimer’s disease (AD) diagnosis often requires invasive examinations (e.g., liquor analyses), expensive tools (e.g., brain imaging) and highly specialized personnel. The diagnosis commonly is established when the disorder has already caused severe brain damage, and the clinical signs begin to be apparent. Instead, accessible and low-cost approaches for early identification of subjects at high risk for developing AD years before they show overt symptoms are fundamental to provide a critical time window for more effective clinical management, treatment, and care planning. This article proposes an ensemble-based machine learning algorithm for predicting AD development within 9 years from first overt signs and using just five clinical features that are easily detectable with neuropsychological tests. The validation of the system involved both healthy individuals and mild cognitive impairment (MCI) patients drawn from the ADNI open dataset, at variance with previous studies that considered only MCI. The system shows higher levels of balanced accuracy, negative predictive value, and specificity than other similar solutions. These results represent a further important step to build a preventive fast-screening machine-learning-based tool to be used as a part of routine healthcare screenings.