Thesis Details
Klasifikace pohybových abnormalit pomocí genetického programování
When suppressing the symptoms of Parkinson's disease, the correct dosage of drugs is critical for the patient. Improper dosing can either cause insufficient suppression of symptoms or, conversely, side effects, such as dyskinesia, occur with high doses. Dyskinesia is manifested by involuntary muscle movement. This work deals with the automated classification of dyskinesia from motion data recorded using a triaxial accelerometer located on the patient's body. In this work, the classifier of dyskinesia is automatically designed using Cartesian genetic programming. The designed classifier achieves very good quality of classification of severe dyskinesia (AUC = 0,94), which is a comparable result to the techniques presented in scientific literature.
Machine learning, Parkinson's disease, evolutionary algorithm, cartesian genetic programming, dyskinesia, classifier, evolution.
Holík Lukáš, doc. Mgr., Ph.D. (DITS FIT BUT), člen
Hradiš Michal, Ing., Ph.D. (DCGM FIT BUT), člen
Jaroš Jiří, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Křivka Zbyněk, Ing., Ph.D. (DIFS FIT BUT), člen
@bachelorsthesis{FITBT23724, author = "Ale\v{s} Chud\'{a}rek", type = "Bachelor's thesis", title = "Klasifikace pohybov\'{y}ch abnormalit pomoc\'{i} genetick\'{e}ho programov\'{a}n\'{i}", school = "Brno University of Technology, Faculty of Information Technology", year = 2021, location = "Brno, CZ", language = "czech", url = "https://www.fit.vut.cz/study/thesis/23724/" }