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Article

A Combined Visualization Method for Multivariate Data Analysis. Application to Knee Kinematic and Clinical Parameters Relationships

1
Centre de Recherche LICEF, TELUQ University, Montréal, QC H2S 3L5, Canada
2
Laboratoire de recherche en imagerie et orthopédie (LIO), CRCHUM, Montréal, QC H2X 0A9, Canada
3
Division of Orthopaedic Surgery, Dalhousie University, Halifax, NS B3H 4R2, Canada
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(5), 1762; https://doi.org/10.3390/app10051762
Received: 28 January 2020 / Revised: 25 February 2020 / Accepted: 28 February 2020 / Published: 4 March 2020
(This article belongs to the Special Issue The Application of Data Mining to Health Data)
This paper aims to analyze the correlation structure between the kinematic and clinical parameters of an end-staged knee osteoarthritis population. The kinematic data are a set of characteristics derived from 3D knee kinematic patterns. The clinical parameters include the answers of a clinical questionnaire and the patient’s demographic characteristics. The proposed method performs, first, a regularized canonical correlation analysis (RCCA) to evaluate the multivariate relationship between the clinical and kinematic datasets, and second, a combined visualization method to better understand the relationships between these multivariate data. Results show the efficiency of using different and complementary visual representation tools to highlight hidden relationships and find insights in data. View Full-Text
Keywords: regularized canonical correlation analysis (RCCA); multivariate data mining; kinematic data; clinical data; gait analysis; knee osteoarthritis (OA) regularized canonical correlation analysis (RCCA); multivariate data mining; kinematic data; clinical data; gait analysis; knee osteoarthritis (OA)
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MDPI and ACS Style

Bensalma, F.; Richardson, G.; Ouakrim, Y.; Fuentes, A.; Dunbar, M.; Hagemeister, N.; Mezghani, N. A Combined Visualization Method for Multivariate Data Analysis. Application to Knee Kinematic and Clinical Parameters Relationships. Appl. Sci. 2020, 10, 1762. https://doi.org/10.3390/app10051762

AMA Style

Bensalma F, Richardson G, Ouakrim Y, Fuentes A, Dunbar M, Hagemeister N, Mezghani N. A Combined Visualization Method for Multivariate Data Analysis. Application to Knee Kinematic and Clinical Parameters Relationships. Applied Sciences. 2020; 10(5):1762. https://doi.org/10.3390/app10051762

Chicago/Turabian Style

Bensalma, Fatima, Glen Richardson, Youssef Ouakrim, Alexandre Fuentes, Michael Dunbar, Nicola Hagemeister, and Neila Mezghani. 2020. "A Combined Visualization Method for Multivariate Data Analysis. Application to Knee Kinematic and Clinical Parameters Relationships" Applied Sciences 10, no. 5: 1762. https://doi.org/10.3390/app10051762

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