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 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.
Highlights
Data visualization and multivariate data analysis are an active and current research area in applied statistics, engineering, and data mining
We investigate the multivariate relationship between knee kinematics and clinical parameters of patients who suffer from end-stage knee osteoarthritis (OA)
Correlate with the functional capacity of the patient, (2) supply additional, more relevant information than the clinical examination, (3) be accurate and repeatable, (4) result from a test which does not alter the natural performance of the patient, and (5) be interpreted by experienced clinicians [6]. In this context of clinical gait analysis, we propose a technical approach that aims at understanding the association between kinematic measurements and clinical data related to the functional status of the patient
Summary
Data visualization and multivariate data analysis are an active and current research area in applied statistics, engineering, and data mining. They become an increasingly popular area for displaying and exploring complex and multidimensional data involving several application domains (finances, engineering, and healthcare) in which the relationships between many attributes are of vital interest [1,2]. Multivariate statistical methods are designed to simultaneously analyze data sets of multiple variables and are used to model different forms of relationships among variables. Multivariate data visualization is strongly motivated to investigate the interrelationships between different data attributes, to identify, cluster, and correlate the underlying data
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