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RESEARCH PRODUCT
Discovering the Discriminating Power in Patient Test Features Using Visual Analytics: A Case Study in Parkinson’s Disease
Panagiotis MoschonasAnastasios DrosouElias KalamarasCharalambos PapaxanthisSevasti BostantjopoulouVassilia HatzitakiKonstantinos VotisDimitrios TzovarasZoe KatsarouStavros Papadopoulossubject
Visual analytics[ INFO ] Computer Science [cs]Parkinson's diseaseComputer science02 engineering and technology[INFO] Computer Science [cs]Machine learningcomputer.software_genre03 medical and health sciences0302 clinical medicineMulti-objective optimisation0202 electrical engineering electronic engineering information engineeringmedicineFeature (machine learning)[INFO]Computer Science [cs]In patient[SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]Modalitiesbusiness.industryVisual analyticsFeature discrimination powermedicine.diseaseTest (assessment)Power (physics)Identification (information)[ SDV.NEU ] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]Parkinson’s disease[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]020201 artificial intelligence & image processingData miningArtificial intelligencebusinesscomputer030217 neurology & neurosurgerydescription
Part 11: New Methods and Tools for Big Data Wokshop (MT4BD); International audience; 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.
year | journal | country | edition | language |
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2016-09-16 |