Discovering the Discriminating Power in Patient Test Features Using Visual Analytics: A Case Study in Parkinson's Disease

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.

• Publication year

  2016

• Venue

  Artificial Intelligence Applications and Innovations

• Publication date

  2016-09-16

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1007/978-3-319-44944-9_53
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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