%0 Conference Paper %1 7897301 %A Tsiouris, K. M. %A Rigas, G. %A Antonini, A. %A Gatsios, D. %A Konitsiotis, S. %A Koutsouris, D. D. %A Fotiadis, D. I. %B 2017 IEEE EMBS International Conference on Biomedical Health Informatics (BHI) %D 2017 %K data_mining %P 445-448 %R 10.1109/BHI.2017.7897301 %T Mining motor symptoms UPDRS data of Parkinson's disease patients for the development of Hoehn and Yahr estimation decision support system %U http://ieeexplore.ieee.org/document/7897301/ %X In this work a decision support system (DSS) for the conversion of Unified Parkinson's Disease Rating Scale (UPDRS) motor symptoms into a Hoehn & Yahr stage representation is proposed. Accurate estimation of a Parkinson's Disease patient's Hoehn & Yahr stage is of great importance since this single value is enough to represent condition, severity of symptoms and localization and disease progression. For the first time data mining techniques are used to enhance Hoehn & Yahr stage estimation performance in a DSS. In its core a classification algorithm is trained using motor evaluation UPDRS data and new instances can then be automatically classified to provide suggestions and facilitate the clinician's final decision. Different classification methods and feature evaluation approaches are evaluated using public UPDRS data from the Parkinson's Progression Markers Initiative (PPMI). Overall, the Hoehn & Yahr stage classification accuracy reaches 87%.

@inproceedings{7897301, abstract = {In this work a decision support system (DSS) for the conversion of Unified Parkinson's Disease Rating Scale (UPDRS) motor symptoms into a Hoehn & Yahr stage representation is proposed. Accurate estimation of a Parkinson's Disease patient's Hoehn & Yahr stage is of great importance since this single value is enough to represent condition, severity of symptoms and localization and disease progression. For the first time data mining techniques are used to enhance Hoehn & Yahr stage estimation performance in a DSS. In its core a classification algorithm is trained using motor evaluation UPDRS data and new instances can then be automatically classified to provide suggestions and facilitate the clinician's final decision. Different classification methods and feature evaluation approaches are evaluated using public UPDRS data from the Parkinson's Progression Markers Initiative (PPMI). Overall, the Hoehn & Yahr stage classification accuracy reaches 87%.}, added-at = {2018-01-18T18:30:51.000+0100}, author = {Tsiouris, K. M. and Rigas, G. and Antonini, A. and Gatsios, D. and Konitsiotis, S. and Koutsouris, D. D. and Fotiadis, D. I.}, biburl = {https://www.bibsonomy.org/bibtex/20b772163f3954bdb46645e21e30f5953/defeatnelly}, booktitle = {2017 IEEE EMBS International Conference on Biomedical Health Informatics (BHI)}, doi = {10.1109/BHI.2017.7897301}, interhash = {d97b27ec48ad796069132ff864eed662}, intrahash = {0b772163f3954bdb46645e21e30f5953}, keywords = {data_mining}, month = feb, pages = {445-448}, timestamp = {2018-01-18T18:30:51.000+0100}, title = {Mining motor symptoms UPDRS data of Parkinson's disease patients for the development of Hoehn and Yahr estimation decision support system}, url = {http://ieeexplore.ieee.org/document/7897301/}, year = 2017 }