A novel data preprocessing method for boosting neural network performance: A case study in osteoporosis prediction

Iliou, Theodoros; Anagnostopoulos, Christos‐Nikolaos; Stephanakis, Ioannis M.; Anastassopoulos, George

doi:10.1016/j.ins.2015.10.026

Cited by 26 publications

(21 citation statements)

References 13 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: Discussionsupporting

confidence: 81%

“…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: Discussionsupporting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

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

Kavitha¹,

Ganeshkumar²,

Park³

et al. 2016

Dentomaxillofacial Radiology

View full text Add to dashboard Cite

show abstract

“…More than 200 million individuals suffer from osteoporosis worldwide [5], and there are about 9 million osteoporotic fractures happening each year [6], which represents a burden for society [7,8]. In osteoporosis, there is reduction of the Bone Mineral Density (BMD) and disruption of the bone micro-architecture [9]. However, there are lack of direct methods to determine bone strength.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural network in the diagnostic system of osteoporosis

Xiang

2016

Neurocomputing

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“…Several studies were conducted in 2015, Iliou et al [ 31 ], used a set of 589 records extracted from the Greek population, who performed bone and laboratory densitometry examinations, applying a multilayer perceptron classifier with the tenfold cross-validation method. In this study, we considered three and five diagnostic factors for the prediction of osteoporosis risk, classifying them into three categories: normal, osteopenia and osteoporosis.…”

Section: Resultsmentioning

confidence: 99%

“…Regarding the number of variables Iliou et al [ 31 ], considered 35 parameters for the prediction of osteoporosis risk, to identify the most significant parameters, in this paper, a cross-validation method was used 10 times. With this data set it was possible to categorize individuals into three classes: normal, osteopenia and osteoporosis.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence on the identification of risk groups for osteoporosis, a general review

et al. 2018

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IntroductionThe goal of this paper is to present a critical review on the main systems that use artificial intelligence to identify groups at risk for osteoporosis or fractures. The systems considered for this study were those that fulfilled the following requirements: range of coverage in diagnosis, low cost and capability to identify more significant somatic factors.MethodsA bibliographic research was done in the databases, PubMed, IEEExplorer Latin American and Caribbean Center on Health Sciences Information (LILACS), Medical Literature Analysis and Retrieval System Online (MEDLINE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, Web of Science, and Science Direct searching the terms “Neural Network”, “Osteoporosis Machine Learning” and “Osteoporosis Neural Network”. Studies with titles not directly related to the research topic and older data that reported repeated strategies were excluded. The search was carried out with the descriptors in German, Spanish, French, Italian, Mandarin, Portuguese and English; but only studies written in English were found to meet the established criteria. Articles covering the period 2000–2017 were selected; however, articles prior to this period with great relevance were included in this study.DiscussionBased on the collected research, it was identified that there are several methods in the use of artificial intelligence to help the screening of risk groups of osteoporosis or fractures. However, such systems were limited to a specific ethnic group, gender or age. For future research, new challenges are presented.ConclusionsIt is necessary to develop research with the unification of different databases and grouping of the various attributes and clinical factors, in order to reach a greater comprehensiveness in the identification of risk groups of osteoporosis. For this purpose, the use of any predictive tool should be performed in different populations with greater participation of male patients and inclusion of a larger age range for the ones involved. The biggest challenge is to deal with all the data complexity generated by this unification, developing evidence-based standards for the evaluation of the most significant risk factors.

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A novel data preprocessing method for boosting neural network performance: A case study in osteoporosis prediction

Cited by 26 publications

References 13 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

Application of artificial neural network in the diagnostic system of osteoporosis

Artificial intelligence on the identification of risk groups for osteoporosis, a general review

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