Mimicking the immune system to diagnose Parkinson's disease from handwriting

We introduce a method adopting the Negative Selection Algorithm, which mimics the way the human immune system learns to discriminate body cells from external antigens, for the computer-aided diagnosis of Parkinson's disease from online handwriting. The major advantage of the proposed method wit...

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Published in:International Conference on Pattern Recognition pp. 2496 - 2502
Main Authors: Parziale, Antonio, Cioppa, Antonio Della, Marcelli, Angelo
Format: Conference Proceeding
Language:English
Published: IEEE 21.08.2022
Subjects:
Behavioral sciences
Detectors
Feature extraction
Parkinson's disease
Sociology
Support vector machines
Training
ISSN:2831-7475
Online Access:Get full text
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Abstract We introduce a method adopting the Negative Selection Algorithm, which mimics the way the human immune system learns to discriminate body cells from external antigens, for the computer-aided diagnosis of Parkinson's disease from online handwriting. The major advantage of the proposed method with respect to the current state-of-the-art machine learning methods is that it is trained only on data from healthy subjects, thus avoiding the burden of collecting patients' data. Moreover, it has only two parameters to set, and its implementation is by far simpler than those of most of, if not all, the methods proposed in the literature. The performance of the proposed method is evaluated on the PaHaW dataset, which includes handwriting samples drawn by 75 subjects. The results show that it outperforms the state-of-the-art methods and uses fewer features.
AbstractList We introduce a method adopting the Negative Selection Algorithm, which mimics the way the human immune system learns to discriminate body cells from external antigens, for the computer-aided diagnosis of Parkinson's disease from online handwriting. The major advantage of the proposed method with respect to the current state-of-the-art machine learning methods is that it is trained only on data from healthy subjects, thus avoiding the burden of collecting patients' data. Moreover, it has only two parameters to set, and its implementation is by far simpler than those of most of, if not all, the methods proposed in the literature. The performance of the proposed method is evaluated on the PaHaW dataset, which includes handwriting samples drawn by 75 subjects. The results show that it outperforms the state-of-the-art methods and uses fewer features.
Author Cioppa, Antonio Della
Marcelli, Angelo
Parziale, Antonio
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  givenname: Antonio Della
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  givenname: Angelo
  surname: Marcelli
  fullname: Marcelli, Angelo
  email: amarcelli@unisa.it
  organization: University of Salerno,DIEM,Fisciano,SA,Italy,84084
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Snippet We introduce a method adopting the Negative Selection Algorithm, which mimics the way the human immune system learns to discriminate body cells from external...
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StartPage 2496
SubjectTerms Behavioral sciences
Detectors
Feature extraction
Parkinson's disease
Sociology
Support vector machines
Training
Title Mimicking the immune system to diagnose Parkinson's disease from handwriting
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