title: The application of NCaRBS to the Trendelenburg test and total hip arthroplasty outcome.
abstract: This paper compares the frontal plane hip function of subject's known to have had hip arthroplasty via either the lateral (LA) or posterior (PA) surgical approaches and a group of subjects associated with no pathology (NP). This is investigated through the Trendelenburg test using 3D motion analysis and classification. Here, a recent development on the Classification and Ranking Belief Simplex (CaRBS) technique, able to undertake n-state classification, so termed NCaRBS is employed. The relationship between post-operative hip function measured during a Trendelenburg Test using three patient characteristics (pelvic obliquity, frontal plane hip moment and frontal plane hip power) of LA, PA and NP subjects are modelled together. Using these characteristics, the classification accuracy was 93.75% for NP, 57.14% for LA, 38.46% for PA. There was a clear distinction between NP and post-surgical function. 3/6 LA subjects and 6/8 PA subjects were misclassified as having NP function, implying that greater function is restored following the PA to surgery. NCaRBS achieved a higher accuracy (65.116%) than through a linear discriminant analysis (48.837%). A Neural Network with two-nodes achieved the same accuracy (65.116%) and as expected was further improved with three-nodes (69.767%). A valuable benefit to the employment of the NCaRBS technique is the graphical exposition of the contribution of patient characteristics to the classification analysis.
authors.affiliation: Cardiff School of Engineering, Cardiff University, Queen's Buildings, The Parade, Cardiff, CF24 3AA, UK, whatlinggm@cardiff.ac.uk.
authors.forename: Gemma Marie
authors.initials: GM
authors.lastname: Whatling
authors.forename: C A
authors.initials: CA
authors.lastname: Holt
authors.forename: M J
authors.initials: MJ
authors.lastname: Beynon
mesh_terms: D019644:Arthroplasty, Replacement, Hip; D016002:Discriminant Analysis; D006801:Humans; D008954:Models, Biological; D016571:Neural Networks, Computer; D011187:Posture