The application of NCaRBS to the Trendelenburg test and total hip arthroplasty outcome.

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.

A CaRBS analysis of hip replacement approaches and non-pathology.

This study investigates the differences in hip biomechanics for subjects following a total hip arthroplasty (THA), through the lateral approach (LA) and posterior approach (PA), to those with no pathology (NP). The principal component analysis was performed on two kinematic and two kinetic waveforms (subject-based characteristics) from level gait to identify salient portions of the waveforms for comparison between the subject cohorts. These were classified to identify the differences between post-THA and non-pathological cohorts. The primary technique exposited in the THA analysis is classification and ranking belief simplex (CaRBS). Within the analysis, from the configuration of a CaRBS model, there is discussion on the model fit and contribution of the subject-based characteristics. Where appropriate, comparisons to the CaRBS model are made with the results from a logistic regression (LR) analysis. In terms of model fit, using CaRBS, 24 out of 27 LA/PA subjects (88.89%) and 13 out of 16 NP subjects (81.25%) were correctly classified as exhibiting either post-THA or NP hip functional characteristics during level gait, combining to 86.05% classification accuracy, compared with 81.40% classification accuracy when using LR.

An illustration of variable precision rough set theory: the gender classification of the European barn swallow (Hirundo rustica).

This paper introduces a new technique in the investigation of object classification and illustrates the potential use of this technique for the analysis of a range of biological data, using avian morphometric data as an example. The nascent variable precision rough sets (VPRS) model is introduced and compared with the decision tree method ID3 (through a 'leave n out' approach), using the same dataset of morphometric measures of European barn swallows (Hirundo rustica) and assessing the accuracy of gender classification based on these measures. The results demonstrate that the VPRS model, allied with the use of a modern method of discretization of data, is comparable with the more traditional non-parametric ID3 decision tree method. We show that, particularly in small samples, the VPRS model can improve classification and to a lesser extent prediction aspects over ID3. Furthermore, through the 'leave n out' approach, some indication can be produced of the relative importance of the different morphometric measures used in this problem. In this case we suggest that VPRS has advantages over ID3, as it intelligently uses more of the morphometric data available for the data classification, whilst placing less emphasis on variables with low reliability. In biological terms, the results suggest that the gender of swallows can be determined with reasonable accuracy from morphometric data and highlight the most important variables in this process. We suggest that both analysis techniques are potentially useful for the analysis of a range of different types of biological datasets, and that VPRS in particular has potential for application to a range of biological circumstances.