Abstract
Parkinson’s disease is a chronic neurodegenerative disease affecting people worldwide. Parkinson’s disease patients experience motor symptoms and many other symptoms that affect the quality of their lives. Discovering groups of patients with similar symptoms from different symptom groups can improve the understanding of this incurable disease and advance the development of personalized treatment of Parkinson’s disease patients. This paper proposes a multi-view clustering approach to discover groups of patients experiencing similar symptoms from different symptom groups (views). For that we modified ReliefF feature ranking algorithm to characterize subsets of most informative symptoms that maximize the similarity between the detected patient groups, described by symptoms from different views (i.e. different symptom groups). The adapted mvReliefF algorithm calculates the weight of features based on the values of their neighbors over multiple views. The current approach works for two views simultaneously, but can be extended to multiple views. The results of the experiments show that the proposed methodology, applied on a pair of data sets from the PPMI data collection, successfully identified lists of most important symptoms that divide patients into groups, ordered by the severity of patients’ symptoms.
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Notes
- 1.
MDS-UPDRS denotes Movement Disorder Society-sponsored revision of Unified Parkinson’s Disease Rating Scale.
- 2.
Alternatively, the clustering algorithm and the number of clusters could be determined based on the value of the silhouette score [11]—a normalized measure for cluster quality; these experiments are left for further work.
- 3.
We chose the pair (MDS-UPDRS Part II, MDS-UPDRS Part III) for this analysis as it has the highest similarity with the cluster labels of groups of patients with similar overall status presented in [14].
- 4.
For reference, prior to normalization, feature NP2FREZ from MDS-UPDRS Part II has weight 42485.06, while feature NP3HMOVR from MDS-UPDRS Part III has weight of 32853.21.
- 5.
- 6.
NP3BRADY, NP3TTAPL, NP3RTCON, NP3FACXP, NP3FTAPL, NP3PRSPL, NP3TTAPR, NP3PRSPR, and NP2HWRT.
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Acknowledgments
We are grateful to Tadej Magajna who implemented the mvReliefF algorithm as part of his M.Sc thesis under supervision of Marko Robnik-Šikonja. The research was supported by the Slovenian Research Agency (research core funding programs P2-0209, P6-0411 and P2-0103, and project N2-0078) and the Slovenian Ministry of Education, Science and Sport (project R 2.1 - Public call for the promotion of researchers at the beginning of a career 2.1). Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) (www.ppmi-info.org/data).
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A Rules Describing Clusters of Patients on the Concatenated Data Set
A Rules Describing Clusters of Patients on the Concatenated Data Set
Classification rules describing clusters obtained on the data set concatenated from the MDS-UPDRS Part II and MDS-UPDRS Part III are presented in Table 4. The rules are constructed using the Weka implementation of the Ripper [1] algorithm, with its default parameters.
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Valmarska, A., Miljkovic, D., Lavrač, N., Robnik–Šikonja, M. (2020). Multi-view Clustering with mvReliefF for Parkinson’s Disease Patients Subgroup Detection. In: Michalowski, M., Moskovitch, R. (eds) Artificial Intelligence in Medicine. AIME 2020. Lecture Notes in Computer Science(), vol 12299. Springer, Cham. https://doi.org/10.1007/978-3-030-59137-3_26
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