scholarly journals Assessing Changes in Diabetic Retinopathy Caused by Diabetes Mellitus and Glaucoma Using Support Vector Machines in Combination with Differential Evolution Algorithm

2021 ◽  
Vol 11 (9) ◽  
pp. 3944
Author(s):  
Nicoleta Anton ◽  
Elena Niculina Dragoi ◽  
Filip Tarcoveanu ◽  
Roxana Elena Ciuntu ◽  
Cătălin Lisa ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
Support Vector Machines ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Angle Closure ◽  
Classification Error ◽  
Support Vector ◽  
Venous Dilatation ◽  
Vector Machines

The aim of this study is to evaluate the changes related to diabetic retinopathy (DR) (no changes, small or moderate changes) in patients with glaucoma and diabetes using artificial intelligence instruments: Support Vector Machines (SVM) in combination with a powerful optimization algorithm—Differential Evolution (DE). In order to classify the DR changes and to make predictions in various situations, an approach including SVM optimized with DE was applied. The role of the optimizer was to automatically determine the SVM parameters that lead to the lowest classification error. The study was conducted on a sample of 52 patients: particularly, 101 eyes with glaucoma and diabetes mellitus, in the Ophthalmology Clinic I of the “St. Spiridon” Clinical Hospital of Iaşi. The criteria considered in the modelling action were normal or hypertensive open-angle glaucoma, intraocular hypertension and associated diabetes. The patients with other types of glaucoma pseudoexfoliation, pigment, cortisone, neovascular and primitive angle-closure, and those without associated diabetes, were excluded. The assessment of diabetic retinopathy changes were carried out with Volk lens and Fundus Camera Zeiss retinal photography on the dilated pupil, inspecting all quadrants. The criteria for classifying the DR (early treatment diabetic retinopathy study—ETDRS) changes were: without changes (absence of DR), mild forma nonproliferative diabetic retinopathy (the presence of a single micro aneurysm), moderate form (micro aneurysms, hemorrhages in 2–3 quadrants, venous dilatations and soft exudates in a quadrant), severe form (micro aneurysms, hemorrhages in all quadrants, venous dilatation in 2–3 quadrants) and proliferative diabetic retinopathy (disk and retinal neovascularization in different quadrants). Any new clinical element that occurred in subsequent checks, which led to their inclusion in severe nonproliferative or proliferative forms of diabetic retinopathy, was considered to be the result of the progression of diabetic retinopathy. The results obtained were very good; in the testing phase, a 95.23% accuracy has been obtained, only one sample being wrongly classified. The effectiveness of the classification algorithm (SVM), developed in optimal form with DE, and used in predictions of retinal changes related to diabetes, was demonstrated.

Least squares support vector machines with tuning based on chaotic differential evolution approach applied to the identification of a thermal process

2012 ◽  
Vol 39 (5) ◽  
pp. 4805-4812 ◽  
Author(s):  
Glauber Souto dos Santos ◽  
Luiz Guilherme Justi Luvizotto ◽  
Viviana Cocco Mariani ◽  
Leandro dos Santos Coelho
Keyword(s):  
Support Vector Machines ◽  
Differential Evolution ◽  
Least Squares ◽  
Thermal Process ◽  
Support Vector ◽  
Vector Machines ◽  
Evolution Approach

scholarly journals Pattern classification of time-series signals using Fisher kernels and support vector machines

2021 ◽  
Author(s):  
Yashodhan Rajiv Athavale
Keyword(s):  
Time Series ◽  
Support Vector Machines ◽  
Stock Market ◽  
Stock Prices ◽  
Binary Classification ◽  
Gaussian Mixture ◽  
Classification Error ◽  
Support Vector ◽  
Classification Error Rate ◽  
Vector Machines

The objective of this study is to assess the performance and capability of a kernel-based machine learning method for time-series signal classification. Applying various stages of dimension transformation, training, testing and cross-validation, we attempt to perform a binary classification using the time-series signals from each category. This study has been applied to two domains: Financial and Biomedical. The financial domain study involves identifying the possibility of collapse or survival of a company trading in the stock market. For assessing the fate of each company, we collect its real stock market data, which is basically a financial time-series composed of weekly closing stock prices in a common time-series interval. This study has been applied to various economic sectors such as Pharmaceuticals and Biotechnology, Automobiles, Oil & Gas, Water Supply etc. The data has been collected using Thomson’s Datastream software. In the biomedical study we are dealing with knee signals collected using the Vibration arthrometry technique. This study involves using the severity of cartilage degeneration for assessing the possibility omachinf a subject getting affected by Osteoarthritis or undergoing knee replacement surgery at a later stage. This non-invasive diagnostic method can also prove be an alternative to various invasive procedures used for detecting osteoarthritis. For this analysis we have used the vibroarthro-signals for about 38 abnormal and 51 normal knee joint case studies. In both studies we apply Fisher Kernels incorporated with Gaussian Mixture Model (GMM) for dimension transformation into feature space created as a three-dimensional plot for visualization. The transformed data is then trained and tested using support vector machines for performing binary classification. From our experiments we observe that our method fits really well for both the studies with the classification error rate between 10% to 15%.

scholarly journals Pattern classification of time-series signals using Fisher kernels and support vector machines

2021 ◽  
Author(s):  
Yashodhan Rajiv Athavale
Keyword(s):  
Time Series ◽  
Support Vector Machines ◽  
Stock Market ◽  
Stock Prices ◽  
Binary Classification ◽  
Gaussian Mixture ◽  
Classification Error ◽  
Support Vector ◽  
Classification Error Rate ◽  
Vector Machines

The objective of this study is to assess the performance and capability of a kernel-based machine learning method for time-series signal classification. Applying various stages of dimension transformation, training, testing and cross-validation, we attempt to perform a binary classification using the time-series signals from each category. This study has been applied to two domains: Financial and Biomedical. The financial domain study involves identifying the possibility of collapse or survival of a company trading in the stock market. For assessing the fate of each company, we collect its real stock market data, which is basically a financial time-series composed of weekly closing stock prices in a common time-series interval. This study has been applied to various economic sectors such as Pharmaceuticals and Biotechnology, Automobiles, Oil & Gas, Water Supply etc. The data has been collected using Thomson’s Datastream software. In the biomedical study we are dealing with knee signals collected using the Vibration arthrometry technique. This study involves using the severity of cartilage degeneration for assessing the possibility omachinf a subject getting affected by Osteoarthritis or undergoing knee replacement surgery at a later stage. This non-invasive diagnostic method can also prove be an alternative to various invasive procedures used for detecting osteoarthritis. For this analysis we have used the vibroarthro-signals for about 38 abnormal and 51 normal knee joint case studies. In both studies we apply Fisher Kernels incorporated with Gaussian Mixture Model (GMM) for dimension transformation into feature space created as a three-dimensional plot for visualization. The transformed data is then trained and tested using support vector machines for performing binary classification. From our experiments we observe that our method fits really well for both the studies with the classification error rate between 10% to 15%.

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