A combined visualization method for multivariate data analysis. Application to knee kinematic and clinical parameters relationships

Fatima Bensalma; Glen Richardson; Youssef Ouakrim; Alexandre Fuentes; Michael Dunbar; Nicola Hagemeister; Neila Mezghani

doi:10.3390/app10051762

A combined visualization method for multivariate data analysis. Application to knee kinematic and clinical parameters relationships

Fatima Bensalma, Glen Richardson, Youssef Ouakrim, Alexandre Fuentes, Michael Dunbar, Nicola Hagemeister, Neila Mezghani

Medicine

Medicine

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

2 Citas (Scopus)

Resumen

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 themultivariate 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.

Idioma original	English
Número de artículo	1762
Publicación	Applied Sciences (Switzerland)
Volumen	10
N.º	5
DOI	https://doi.org/10.3390/app10051762
Estado	Published - mar. 1 2020

Nota bibliográfica

Funding Information:
This research was supported by the Canada Research Chair on Biomedical Data Mining (950-231214).

Publisher Copyright:
© 2020 by the authors.

ASJC Scopus Subject Areas

General Materials Science
Instrumentation
General Engineering
Process Chemistry and Technology
Computer Science Applications
Fluid Flow and Transfer Processes

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abstract = "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 themultivariate 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.",

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AU - Bensalma, Fatima

AU - Richardson, Glen

AU - Ouakrim, Youssef

AU - Fuentes, Alexandre

AU - Dunbar, Michael

AU - Hagemeister, Nicola

AU - Mezghani, Neila

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N2 - 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 themultivariate 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.

AB - 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 themultivariate 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.

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A combined visualization method for multivariate data analysis. Application to knee kinematic and clinical parameters relationships

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ASJC Scopus Subject Areas

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