Comparison of Classification Algorithms with Wrapper-Based Feature Selection for Predicting Osteoporosis Outcome Based on Genetic Factors in a Taiwanese Women Population

Chang, Hsueh-Wei; Chiu, Yu-Wen; Kao, Hao-Yun; Yang, Cheng‐Hong; Ho, Wen-Hsien

doi:10.1155/2013/850735

Cited by 25 publications

(16 citation statements)

References 39 publications

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“…10 They have reported an increased classification performance with an AUC of 0.951 for 15 hidden layers, which is similar to the AUC of 0.962 at lumbar spine BMD and slightly lower than the AUC of 0.986 at femoral neck BMD evaluated in this study. The AUC (0.631) for the wrapper-based feature selection method was found to be higher than that without it (0.489) for identifying females with osteoporosis, 13 …”

Section: Discussionmentioning

confidence: 90%

“…In our previously proposed SVM model, 37 the average and variance of the mandibular cortical width were utilized for differential diagnosis, which resulted in a much lower sensitivity and specificity of 90% and 69.6%, respectively, with femoral neck BMD. Chang et al 13 obtained a lower sensitivity (57.9%) and higher specificity (68.9%) from a multilayer feed-forward neural network using feature selection. Our newly proposed diagnostic system using optimized GSF classifier modelling has achieved a higher sensitivity and specificity especially with femoral neck BMD for determining females with normal and osteoporotic subjects compared with existing systems.…”

Section: Discussionmentioning

confidence: 99%

“…5,8,9 However, these models require strong assumptions to predict the relationship between disease risk and each risk factor. In recent years, use of classifier systems like multilayer perceptron, 10 Bayes classifier, 11 random forest classifier, 12 multilayer feed-forward neural network 13 and support vector machine (SVM) based on cortical 14,15 or trabecular bone features 16,17 has been developed for the detection of a low BMD or osteoporosis. Although all these classifiers delivered an acceptable diagnostic accuracy, they did not explain the input variables involved, the interpretations of experimental results or how they produced a predicted outcome.…”

Section: Introductionmentioning

confidence: 99%

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Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches

Kavitha¹,

Ganeshkumar²,

Park³

et al. 2016

Dentomaxillofacial Radiology

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Objectives: This study proposed a new automated screening system based on a hybrid genetic swarm fuzzy (GSF) classifier using digital dental panoramic radiographs to diagnose females with a low bone mineral density (BMD) or osteoporosis. Methods: The geometrical attributes of both the mandibular cortical bone and trabecular bone were acquired using previously developed software. Designing an automated system for osteoporosis screening involved partitioning of the input attributes to generate an initial membership function (MF) and a rule set (RS), classification using a fuzzy inference system and optimization of the generated MF and RS using the genetic swarm algorithm. Fivefold cross-validation (5-FCV) was used to estimate the classification accuracy of the hybrid GSF classifier. The performance of the hybrid GSF classifier has been further compared with that of individual genetic algorithm and particle swarm optimization fuzzy classifiers. Results: Proposed hybrid GSF classifier in identifying low BMD or osteoporosis at the lumbar spine and femoral neck BMD was evaluated. The sensitivity, specificity and accuracy of the hybrid GSF with optimized MF and RS in identifying females with a low BMD were 95.3%, 94.7% and 96.01%, respectively, at the lumbar spine and 99.1%, 98.4% and 98.9%, respectively, at the femoral neck BMD. The diagnostic performance of the proposed system with femoral neck BMD was 0.986 with a confidence interval of 0.942-0.998. The highest mean accuracy using 5-FCV was 97.9% with femoral neck BMD. Conclusions: The combination of high accuracy along with its interpretation ability makes this proposed automatic system using hybrid GSF classifier capable of identifying a large proportion of undetected low BMD or osteoporosis at its early stage.

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Section: Discussionmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches

Kavitha¹,

Ganeshkumar²,

Park³

et al. 2016

Dentomaxillofacial Radiology

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“…The classification accuracy for the ANN classifiers was of less than 83.0%. Even though a wrapper-based feature selection method was used to determine the features, the FFNN model 35 yielded the poorest performance of all existing approaches. On the other hand, the proposed method had superior classification performance, even when the same classical FFNN model was used.…”

Section: Comparison Of Classification Performance Between the Proposementioning

confidence: 99%

Classification of Osteoporosis by Extracting the Microarchitectural Properties of Trabecular Bone from DXA Scans Based on Thresholding Technique

Lee¹,

Kim²,

Hwang³

et al. 2015

j med imaging hlth inform

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The purpose of this study was to find optimal thresholding conditions for diagnosing osteoporosis using a novel thresholding technique. Seven trabecular features-composed of four structural and two skeletonized features as well as the fractal dimension (FD)-were extracted from 2D DXA scans. A binarized image was used to identify the structural features and the FD, and the skeletonized feature sets were obtained from a skeletonized image. The proposed thresholding technique utilizes the percentages for the trabecular bone area as the threshold values after dividing them into ranges from 0% to 95%. Principal component analysis was used to identify the optimum subsets from the original trabecular features. A two-layered feed forward neural network used a Bayesian regularization training algorithm to carry out the classification. In the results of the experiment, the proposed method exhibited superior performance in discriminating osteoporotic patients from normal individuals. The overall accuracy was significantly higher, in the ranges of 10-40%, and a threshold condition of 20% presented the highest classification performance when considering all of the experimental conditions. The accuracy of the classification gradually decreased as the threshold increased beyond 20%. These results suggest that the proposed method has clinical applicability for diagnosing osteoporosis.

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“…Previous researchers have used neural networks which would sequentially add new features [5], or SVM-based approaches which remove one feature at a time from the full dataset [11]. However, while some have compared different classification algorithms within the wrapper framework [1], no previous work has considered how different classification performance metrics within the wrapper can influence the features chosen.…”

Section: Related Workmentioning

confidence: 99%

How the Choice of Wrapper Learner and Performance Metric Affects Subset Evaluation

Wald

Khoshgoftaar

Napolitano

2013

2013 IEEE 25th International Conference on Tools With Artificial Intelligence

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Due to the widespread problem of high dimensionality (datasets with many features/independent attributes), feature selection has become an important research topic in many areas of machine learning. One form of feature selection, wrapper-based subset evaluation, has been the focus of a moderate amount of research, because its use of classification learners to find optimal feature subsets has the potential to remove redundant features and find feature subsets which directly achieve the goal of improving classification performance. However, while the choice of learner to use within the wrapper framework has previously been studied, no paper has thoroughly investigated the role of the performance metric used within the wrapper process. Especially with imbalanced data (data where one class predominates over other classes), traditional metrics such as accuracy can give a misleading view of how many instances from each class are mislabeled. While it seems intuitive that metrics which take balance into account will affect the chosen features, no previous study has investigated this effect directly. In the present work, we test five different learners and five different performance metrics within the wrapper framework and use a newly-proposed variant of the Tanimoto Index to evaluate the similarity among the different choices of learner and metric as all other factors are held constant, using two datasets from the domain of social network profile mining. We find that while the Best Arithmetic Mean and Best Geometric Mean metrics (both of which find the stated means of True Positive Rate and True Negative Rate) are somewhat similar, they still are quite distinct, and no other metrics are particularly similar to one another. The five learners were also found to produce extremely dissimilar feature subsets. Thus, we show that the choice of both learner and metric has a major effect on which features are selected by through wrapper-based feature selection.

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Comparison of Classification Algorithms with Wrapper-Based Feature Selection for Predicting Osteoporosis Outcome Based on Genetic Factors in a Taiwanese Women Population

Cited by 25 publications

References 39 publications

Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches

Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches

Classification of Osteoporosis by Extracting the Microarchitectural Properties of Trabecular Bone from DXA Scans Based on Thresholding Technique

How the Choice of Wrapper Learner and Performance Metric Affects Subset Evaluation

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