Prototypes stability analysis in the design of fuzzy classifiers to assess the severity of scoliosis

Ramirez, L.; Durdle, N.G.; Hill, Douglas L.; Raso, V.J.

doi:10.1109/ccece.2003.1226180

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…E.g., by utilizing the class information to guide the clustering process, some supervised clustering [5][6][7] or semi-supervised clustering algorithms [8][9][10] Generally, clustering and classification learnings are usually formulated by different models or criteria, hence it is relatively difficult to cast both into a single framework. To fuse the advantages of both learners together, many existing algorithms [11][12][13][14][15][16][17][18][19][20] handle the clustering learning and classification learning in a sequential or independent manner. As illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

A Multiobjective Simultaneous Learning Framework for Clustering and Classification

Cai

Chen

Zhang

2010

IEEE Trans. Neural Netw.

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Traditional pattern recognition involves two tasks: clustering learning and classification learning.Clustering result can enhance the generalization ability of classification learning, while the class information can improve the accuracy of clustering learning. Hence, both learning methods can complement each other. To fuse the advantages of both learning methods together, many existing algorithms have been developed in a sequential fusing way by first optimizing the clustering criterion and then the classification criterion associated with the obtained clustering results. However, such kind of algorithms naturally fails to achieve the simultaneous optimality for two criteria, and thus have to sacrifice either the clustering performance or the classification performance. To overcome that problem, in this paper, we present a multi-objective simultaneous learning framework (named MSCC) for both clustering and classification learning. MSCC utilizes multiple objective functions to formulate the clustering and classification problems, respectively, and more importantly it employs the Bayesian theory to make these functions all only dependent on a set of the same parameters, i.e., clustering centers which play a role of the bridge connecting the clustering and classification learning. By simultaneously optimizing the clustering centers embedded in these functions, not only the effective clustering performance but also the promising classification performance can be simultaneously attained. Furthermore, from the multiple Pareto-optimality solutions obtained in MSCC, we can get an interesting observation that there is complementarity to great extent between clustering and classification learning processes. Empirical results on both synthetic and real data sets demonstrate the effectiveness and potential of MSCC.

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Section: Introductionmentioning

confidence: 99%

A Multiobjective Simultaneous Learning Framework for Clustering and Classification

Cai

Chen

Zhang

2010

IEEE Trans. Neural Netw.

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“…In the last decades researchers have been using artificial intelligence techniques to analyze spinal deformities. Ramirez et al (2003) used Fuzzy Logic to develop a classifier system to assess the severity of scoliosis in 41 adolescents. Radiographic images of the volunteers' backs were used.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of scoliosis using baropodometer and artificial neural network

et al. 2017

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Introduction: One of the most recurrent pathologies in the spine is scoliosis. It occurs in the frontal plane and is formed by one or more curves in the spinal column. The scoliosis causes global postural misalignment in an individual. One of the modifications produced by postural misalignment is the way in which an individual distributes weight to the feet. We aimed to implement an electronic system for separating patients with Degree I scoliosis (i.e., 1° to 19° scoliosis according to the Ricard classification) into two groups: C1 (1°-9°) and C2 (10°-9°). The highest percentage of patients with scoliosis is in this range: those who do not need to wear vests or undergo surgery and whose treatment is performed via special physical exercise and frequent evaluations by healthcare professionals. Methods:The electronic system consists of a baropodometer and artificial neural networks (ANNs). The classification of patients in the scoliosis groups was performed with MATLAB software and a Single Layer Perceptron network using the backpropagation training algorithm. Evaluations were performed on 63 volunteers. Results: The mean classification sensitivity was 93.7% in the C1 group and 94.5% in the C2 group. The classification accuracy was 83.3% in the C1 group and 96.0% in the C2 group. Conclusion: The implemented system can contribute to the treatment of patients with scoliosis grades ranging from 1° to 19°, which represents the highest incidence of this pathology, for which the monitoring of the clinical condition using noninvasive techniques is of fundamental importance.

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A fuzzy classifier approach to assessing the progression of adolescent idiopathic scoliosis from radiographic indicators

Ajemba

Ramirez

Durdle

et al.

Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No.04CH37513)

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A fuzzy classijier approach was used to predict the progression of adolescent idiopathic scoliosis (AIS). Past studies indicate that individual indicators of AIS do not reliably predict progression. Complex indicators having improved predictive values have been developed but are unsuitable for clinical use. Based on the hypothesis that combining some common indicators with a fuzzy classifier could produce better results, we conducted a study using radiographic indicators measured from 44 moderate AIS patients. We clustered the data using a fuzzy c-means classifier and designed fuzzy rules to represent each cluster. We classified the records in the dataset using the resulting rules. This approach outperJformed a binaly logistic regression method and a stepwise linear regression method. Less than fifteen minutes per patient is required to measure the indicators, input the data into the system and generate results enabling its use in a clinical environment to aid in the management of AIS.

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Prototypes stability analysis in the design of fuzzy classifiers to assess the severity of scoliosis

Cited by 6 publications

References 5 publications

A Multiobjective Simultaneous Learning Framework for Clustering and Classification

A Multiobjective Simultaneous Learning Framework for Clustering and Classification

Evaluation of scoliosis using baropodometer and artificial neural network

A fuzzy classifier approach to assessing the progression of adolescent idiopathic scoliosis from radiographic indicators

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