Abstract : This paper presents a novel methodology for selecting the most representative features for identifying the presence of the Parkinson’s Disease (PD). The proposed methodology is based on interactive visual analytic based on multi-objective optimisation. The implemented tool processes and visualises the information extracted via performing a typical line-tracking test using a tablet device. Such output information includes several modalities, such as position, velocity, dynamics, etc. Preliminary results depict that the implemented visual analytics technique has a very high potential in discriminating the PD patients from healthy individuals and thus, it can be used for the identification of the best feature type which is representative of the disease presence.
Lazaros Iliadis; Ilias Maglogiannis. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-475, pp.600-610, 2016, Artificial Intelligence Applications and Innovations. 〈http://link.springer.com/chapter/10.1007/978-3-319-44944-9_53〉. 〈10.1007/978-3-319-44944-9_53〉
https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01478299
Contributeur : Caps - Université de Bourgogne
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Dernière modification le : vendredi 8 juin 2018 - 14:50:25
Document(s) archivé(s) le : mercredi 24 janvier 2018 - 01:28:36
Panagiotis Moschonas, Elias Kalamaras, Stavros Papadopoulos, Anastasios Drosou, Konstantinos Votis, et al.. Discovering the Discriminating Power in Patient Test Features Using Visual Analytics: A Case Study in Parkinson’s Disease. Lazaros Iliadis; Ilias Maglogiannis. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-475, pp.600-610, 2016, Artificial Intelligence Applications and Innovations. 〈http://link.springer.com/chapter/10.1007/978-3-319-44944-9_53〉. 〈10.1007/978-3-319-44944-9_53〉. 〈hal-01478299〉
