Development of evolutionary data mining algorithms and their applications to cardiac disease diagnosis

Liu, Jenn-Long; Hsu, Yu-Tzu; Hung, Chih-Lung

doi:10.1109/cec.2012.6256640

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…[26][27][28] A fuzzy classifier is simply a classifier that includes a set of fuzzy membership function (MF) and rule set (RS). It consists of if-then rules characterized by the MF and is adopted and fired using the inference mechanism to derive the output.…”

Section: Fuzzy Classifier Design For Screening Osteoporosismentioning

confidence: 99%

“…GA 25 and PSO 22 are the best known evolutionary algorithms used in several medical diagnoses. 22,[26][27][28] This study focused on developing an automatic osteoporosis diagnostic system that surpassed the defects of existing learning algorithms. The objective of the study was to propose a hybrid genetic swarm fuzzy (GSF) classifier for obtaining simple and interpretable knowledge for a low BMD or osteoporosis from the geometrical attributes of the mandibular cortical and trabecular bone on dental radiographs and also to evaluate the performance of the hybrid GSF classifier compared with that of individual GA and PSO fuzzy classifiers.…”

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: Fuzzy Classifier Design For Screening Osteoporosismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Kavitha¹,

Ganeshkumar²,

Park³

et al. 2016

Dentomaxillofacial Radiology

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“…Jenn Long Liu et al [13] brought up the metamorphic techniques of data mining algorithms to cluster the dataset present in the malady and depict the certainty.…”

Section: Related Workmentioning

confidence: 99%

Data Mining Techniques to Predict Chronic Kidney Disease and its Stages

Nisarga¹,

Kanchana²

2018

IJET

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Chronic Kidney Disease incorporates the state where the kidneys fail to function and reduce the potential to keep a person suffering from the disease healthy. When the condition of the kidneys gets worse, the wastes in the blood are formed in high level. Data mining has been a present pattern for accomplishing analytic outcomes. Colossal measure of un-mined data is gathered by the human services industry so as to find concealed data for powerful analysis and basic leadership. Data mining is the way towards extricating concealed data from gigantic datasets. The goal of our paper is to anticipate CKD utilizing the classification strategy Naïve Bayes. The phases of CKD are anticipated in the light of Glomerular Filtration Rate (GFR).

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Alternative Population Initialization Schemes for Group Search Optimization for Data Clustering

Pacífico

Ludermir

2018

2018 IEEE Congress on Evolutionary Computation (CEC)

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Development of evolutionary data mining algorithms and their applications to cardiac disease diagnosis

Cited by 9 publications

References 8 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

Data Mining Techniques to Predict Chronic Kidney Disease and its Stages

Alternative Population Initialization Schemes for Group Search Optimization for Data Clustering

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