Automatic Diagnosis of Neurodegenerative Diseases: An Evolutionary Approach for Facing the Interpretability Problem
Department of Electrical and Information Engineering and Applied Mathematics, Università degli Studi di Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Italy
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Information 2019, 10(1), 30; https://doi.org/10.3390/info10010030
Received: 11 December 2018
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Revised: 13 January 2019
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Accepted: 14 January 2019
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Published: 17 January 2019
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
Background: The use of Artificial Intelligence (AI) systems for automatic diagnoses is increasingly in the clinical field, being a useful support for the identification of several diseases. Nonetheless, the acceptance of AI-based diagnoses by the physicians is hampered by the black-box approach implemented by most performing systems, which do not clearly state the classification rules adopted. Methods: In this framework we propose a classification method based on a Cartesian Genetic Programming (CGP) approach, which allows for the automatic identification of the presence of the disease, and concurrently, provides the explicit classification model used by the system. Results: The proposed approach has been evaluated on the publicly available HandPD dataset, which contains handwriting samples drawn by Parkinson’s disease patients and healthy controls. We show that our approach compares favorably with state-of-the-art methods, and more importantly, allows the physician to identify an explicit model relevant for the diagnosis based on the most informative subset of features. Conclusion: The obtained results suggest that the proposed approach is particularly appealing in that, starting from the explicit model, it allows the physicians to derive a set of guidelines for defining novel testing protocols and intervention strategies.
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Keywords:
E-health; explainable artificial intelligence; Parkinson disease; machine learning; evolutionary computation
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MDPI and ACS Style
Senatore, R.; Della Cioppa, A.; Marcelli, A. Automatic Diagnosis of Neurodegenerative Diseases: An Evolutionary Approach for Facing the Interpretability Problem. Information 2019, 10, 30. https://doi.org/10.3390/info10010030
AMA Style
Senatore R, Della Cioppa A, Marcelli A. Automatic Diagnosis of Neurodegenerative Diseases: An Evolutionary Approach for Facing the Interpretability Problem. Information. 2019; 10(1):30. https://doi.org/10.3390/info10010030
Chicago/Turabian StyleSenatore, Rosa, Antonio Della Cioppa, and Angelo Marcelli. 2019. "Automatic Diagnosis of Neurodegenerative Diseases: An Evolutionary Approach for Facing the Interpretability Problem" Information 10, no. 1: 30. https://doi.org/10.3390/info10010030
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