A Soft Computing Approach for Osteoporosis Risk Factor Estimation

Mantzaris, Dimitrios; Anastassopoulos, George; Iliadis, Lazaros; Kazakos, Konstantinos; Papadopoulos, Harris

doi:10.1007/978-3-642-16239-8_18

Cited by 11 publications

(4 citation statements)

References 16 publications

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“…The combination of textures and mandibular cortical width based on the SVM model classifier contributed to a better assessment of osteoporosis compared with the use of only individual measurements 15 and reported a 96.8% accuracy, which is lower than our present result with femoral neck BMD. The study by Mantzaris et al 38 applied probabilistic neural networks based on the clinical characteristics of patients, which proved to be an effective potential soft computing technique for the evaluation of osteoporosis risk. It reported a 96.6% accuracy that is almost equal to the 96.0% accuracy with lumbar spine BMD in the present study.…”

Section: Discussionmentioning

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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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: 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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“…They tested 20 different classifiers, which made it possible to select the most important factors which would enable them to classify patients into one of two groups (osteoporosis and no osteoporosis). The same four factors were considered in [ 12 , 13 ], where artificial neural networks were applied to predict fractures. In [ 14 ], a random forest algorithm was applied to evaluate the impact of 15 factors on fracture risk.…”

Section: Related Workmentioning

confidence: 99%

Applications of Artificial Intelligence Methods for the Prediction of Osteoporotic Fractures

Lipnicka

Studniarski

Irzmański

2023

Life

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Background: Osteoporosis is a socio-economic problem of modern aging societies. Bone fractures and the related treatments generate the highest costs. The occurrence of osteoporotic fractures is a cause of chronic disability, many complications, reduced quality of life, and often premature death. Aim of the study: The aim of the study was to determine which of the patient’s potential risk factors pertaining to various diseases and lifestyle have an essential impact on the occurrence of low-energy fractures and the hierarchy of these factors. Methods: The study was retrospective. The documentation of 222 patients (206 women and 16 men) from an osteoporosis treatment clinic in Łódź, Poland was analyzed. Each patient was described by a vector consisting of 27 features, where each feature was a different risk factor. Using artificial neural networks, an attempt was made to create a model that, based on the available data, would be able to predict whether the patient would be exposed to low-energy fractures. We developed a neural network model that achieved the best result for the testing data. In addition, we used other methods to solve the classification problem, i.e., correctly dividing patients into two groups: those with fractures and those without fractures. These methods were logistic regression, k-nearest neighbors and SVM. Results: The obtained results gave us the opportunity to assess the effectiveness of various methods and the importance of the features describing patients. Using logistic regression and the recursive elimination of features, a ranking of risk factors was obtained in which the most important were age, chronic kidney disease, neck T-score, and serum phosphate level. Then, we repeated the learning procedure of the neural network considering only these four most important features. The average mean squared error on the test set was about 27% for the best variant of the model. Conclusions: The comparison of the rankings with different numbers of patients shows that the applied method is very sensitive to changes in the considered data (adding new patients significantly changes the result). Further cohort studies with more patients and more advanced methods of machine learning may be needed to identify other significant risk factors and to develop a reliable fracture risk system. The obtained results may contribute to the improved identification patients at risk of low-energy fractures and early implementation of comprehensive treatment.

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“…Soft Computing is a set of methodologies implementing computational intelligence systems for solving non-linear real world problems, without prior knowledge and symbolic representation of their rules [7] . Artificial Neural Networks, Fuzzy Logic, Evolutionary Programming, Genetic Algorithms, Mimetic Algorithms and Artificial Immune Systems are subfields of soft computing.…”

Section: Introductionmentioning

confidence: 99%

Solar radiation: Cloudiness forecasting using a soft computing approach

Mantzari¹,

Mantzaris

2012

AIR

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Solar energy is one of the most important energy sources with increasing penetration into the power supply systems of many countries, due to the reduced environmental impact of its operation. One of the important factors of the efficiency of photovoltaic systems is the predictability of solar radiation, which depends on the clouds and the meteorological conditions, the occurrence of which is a non-linear process. Prediction of clouds' amount that affects solar radiation using Artificial Neural Networks (ANNs) is presented in this paper. To our knowledge, this approach is the first computational intelligence method that refers to cloudiness forecasting. Input parameter selection is very critical in ANN design. In this study, ten meteorological and temporal variables were selected in the implementation of the ANN structure. The appropriate ANN architecture consists of a hidden layer with hyperbolic tangent sigmoid transfer function and an output layer with a saturating linear transfer function. The most important benefits of the proposed method are the cloudiness forecasting in the next half hour, the embedment of temporal parameters for cloudiness prediction and the highest quantity of data comparatively with other similar studies.

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A Soft Computing Approach for Osteoporosis Risk Factor Estimation

Cited by 11 publications

References 16 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

Applications of Artificial Intelligence Methods for the Prediction of Osteoporotic Fractures

Solar radiation: Cloudiness forecasting using a soft computing approach

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