A Supervised Approach to Classify the Status of Bone Mineral Density in Post-Menopausal Women through Static and Dynamic Baropodometry

Bortone, Ilaria; Trotta, Gianpaolo Francesco; Cascarano, Giacomo Donato; Regina, Paola; Brunetti, Antonio; Feudis, Irio De; Buongiorno, Domenico; Loconsole, Claudio; Bevilacqua, Vitoantonio

doi:10.1109/ijcnn.2018.8489205

Cited by 11 publications

(8 citation statements)

References 34 publications

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“…But this predictive model could be suitable for TBI outcomes and not for other diseases. Ilaria Bortone et.al have proposed a supervised approach to predicate postmenopausal women's BMD status based on alimental habits, lifestyle and precedent fractures [14]. In this approach, a detailed analysis of static and dynamic baropodometry will cull the pertinent features through the Principal component analysis (PCA).…”

Section: Related Workmentioning

confidence: 99%

“…This hybrid classifier model is not suitable for high dimensional medical data classification. Victor Blanco et.al have proposed a novel SVM-based approach towards constructing multi-class classifiers in terms of arranging hyper planes using mixed integer programming by adopting suitable kernel [14]. It can used to handle multi class classification by extending binary SVM classifiers.…”

Section: Related Workmentioning

confidence: 99%

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Clinical Data Based Classification of Osteoporosis and Osteopenia Using Support Vector Machine

Ramesh.T

Santhi

2021

Advances in Parallel Computing

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In the current world millions of people are suffering with bone diseases such as osteoporosis and osteopenia. The early detection of osteoporosis and osteopenia disease is very important as it helps people to be cautious and get treated on time. Hence research on early detection of osteoporosis and osteopenia disease has gained importance across the world. In this paper, analysis of suitable kernel for Support Vector Machine (SVM) focussing on the classification of osteoporosis and osteopenia disease has been carried out and presented. The kernel functions considered includes polynomial, linear, RBF and Gaussian to find the optimal one for the classification of osteoporosis and osteopenia disease with improved accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Clinical Data Based Classification of Osteoporosis and Osteopenia Using Support Vector Machine

Ramesh.T

Santhi

2021

Advances in Parallel Computing

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“…According to the recent trends in the area of intelligent systems for personalized medicine [40–46], it seems clear that there is the necessity for new, low-cost, and accessible technologies to facilitate in-clinic and at-home assessment of motor alterations throughout the progression of PD [47]. In this context, we have proposed a low-cost camera-based system able to recognize and rate PD patients in a completely non-invasive manner.…”

Section: Related Workmentioning

confidence: 99%

A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson’s Disease

Buongiorno

Bortone

Cascarano

et al. 2019

BMC Med Inform Decis Mak

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BackgroundAssessment and rating of Parkinson’s Disease (PD) are commonly based on the medical observation of several clinical manifestations, including the analysis of motor activities. In particular, medical specialists refer to the MDS-UPDRS (Movement Disorder Society – sponsored revision of Unified Parkinson’s Disease Rating Scale) that is the most widely used clinical scale for PD rating. However, clinical scales rely on the observation of some subtle motor phenomena that are either difficult to capture with human eyes or could be misclassified. This limitation motivated several researchers to develop intelligent systems based on machine learning algorithms able to automatically recognize the PD. Nevertheless, most of the previous studies investigated the classification between healthy subjects and PD patients without considering the automatic rating of different levels of severity.MethodsIn this context, we implemented a simple and low-cost clinical tool that can extract postural and kinematic features with the Microsoft Kinect v2 sensor in order to classify and rate PD. Thirty participants were enrolled for the purpose of the present study: sixteen PD patients rated according to MDS-UPDRS and fourteen healthy paired subjects. In order to investigate the motor abilities of the upper and lower body, we acquired and analyzed three main motor tasks: (1) gait, (2) finger tapping, and (3) foot tapping. After preliminary feature selection, different classifiers based on Support Vector Machine (SVM) and Artificial Neural Networks (ANN) were trained and evaluated for the best solution.ResultsConcerning the gait analysis, results showed that the ANN classifier performed the best by reaching 89.4% of accuracy with only nine features in diagnosis PD and 95.0% of accuracy with only six features in rating PD severity. Regarding the finger and foot tapping analysis, results showed that an SVM using the extracted features was able to classify healthy subjects versus PD patients with great performances by reaching 87.1% of accuracy. The results of the classification between mild and moderate PD patients indicated that the foot tapping features were the most representative ones to discriminate (81.0% of accuracy).ConclusionsThe results of this study have shown how a low-cost vision-based system can automatically detect subtle phenomena featuring the PD. Our findings suggest that the proposed tool can support medical specialists in the assessment and rating of PD patients in a real clinical scenario.

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“…Relationships exist among these entities, but an entire network should be assessed. Several works address the problem of analysing medical data by applying data/process mining and simulation [5–7] and in machine learning and deep learning applied in the medical field [8–33].…”

Section: Introductionmentioning

confidence: 99%

Proposal of a health care network based on big data analytics for PDs

Carnimeo

Trotta

Brunetti

et al. 2019

J. eng.

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A Supervised Approach to Classify the Status of Bone Mineral Density in Post-Menopausal Women through Static and Dynamic Baropodometry

Cited by 11 publications

References 34 publications

Clinical Data Based Classification of Osteoporosis and Osteopenia Using Support Vector Machine

Clinical Data Based Classification of Osteoporosis and Osteopenia Using Support Vector Machine

A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson’s Disease

Proposal of a health care network based on big data analytics for PDs

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