Investigating One-Class Classifiers to Diagnose Alzheimer's Disease from Handwriting

• Published in: Image Analysis and Processing - ICIAP 2022 Vol. 13231; pp. 111 - 123
• Main Authors: Parziale, Antonio, Della Cioppa, Antonio, Marcelli, Angelo
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2022; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Alzheimer’s disease diagnosis; Handwriting analysis; Isolation forest; Negative selection algorithm; One-class classifiers; One-class support vector machine
• ISBN: 3031064267; 9783031064265
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-031-06427-2_10

The analysis of handwriting and drawing has been adopted since the early studies to help diagnose neurodegenerative diseases, such as Alzheimer’s and Parkinson’s. Departing from the current state-of-the-art methods that approach the problem of discriminating between healthy subjects and patients by using two- or multi-class classifiers, we propose to adopt one-class classifier models, as they require only data by healthy subjects to build the classifier, thus avoiding to collect patient data, as requested by competing techniques. In this framework, we evaluated the performance of three models of one-class classifiers, namely the Negative Selection Algorithm, the Isolation Forest and the One-Class Support Vector Machine, on the DARWIN dataset, which includes 174 subjects performing 25 handwriting/drawing tasks. The comparison with the state-of-the-art shows that the methods achieve state-of-the-art performance, and therefore may represent a viable alternative to the dominant approach.