Classification of non-chronic and chronic kidney disease using SVM neural networks

2017 ◽  
Vol 7 (1.3) ◽  
pp. 191 ◽  
Author(s):  
Ravindra B.V ◽  
N Sriraam ◽  
M Geetha
Keyword(s):  
Neural Networks ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Classification Accuracy ◽  
The Body ◽  
Support Vector ◽  
Potential Candidate ◽  
Kidney Dialysis ◽  
Sensitivity Specificity

Chronic kidney disease (CKD) refers to the failure of the renal functionalities that leads to the deposition of wastes, electrolytes and other fluids in the body. It is very important to recognize the symptoms that cause the CKD and pathological blood and urine test indicates the key attributes. It is well fact that one has to undergo dialysis due to renal failure. The severity level of disease can be predicted as well as classified using appropriate computer aided quantitative tools. This specific study discusses the classification of chronic and non-chronic kidney disease NCKD using support vector machine (SVM) neural networks. The simulation study makes use of UCI repository CKD datasets with n=400. In order to train to train the attributes of kidney dialysis four cases were considered by including the nominal and numerical values. A radical basis kernel function was employed to train SVM. The performance of the proposed scheme is evaluated in terms of the sensitivity, specificity and classification accuracy. Results reveal an overall classification accuracy of 94.44% was obtained by combining 6 attributes. It can be concluded that the SVM based approach found to be a potential candidate for classification of CKD and NCKD.

scholarly journals A Novel Method for Sea-Land Clutter Separation Using Regularized Randomized and Kernel Ridge Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6491
Author(s):  
Le Zhang ◽  
Jeyan Thiyagalingam ◽  
Anke Xue ◽  
Shuwen Xu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Classification Accuracy ◽  
Signal Amplitude ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Amplitude Change ◽  
Novel Method

Classification of clutter, especially in the context of shore based radars, plays a crucial role in several applications. However, the task of distinguishing and classifying the sea clutter from land clutter has been historically performed using clutter models and/or coastal maps. In this paper, we propose two machine learning, particularly neural network, based approaches for sea-land clutter separation, namely the regularized randomized neural network (RRNN) and the kernel ridge regression neural network (KRR). We use a number of features, such as energy variation, discrete signal amplitude change frequency, autocorrelation performance, and other statistical characteristics of the respective clutter distributions, to improve the performance of the classification. Our evaluation based on a unique mixed dataset, which is comprised of partially synthetic clutter data for land and real clutter data from sea, offers improved classification accuracy. More specifically, the RRNN and KRR methods offer 98.50% and 98.75% accuracy, outperforming the conventional support vector machine and extreme learning based solutions.

Implementation Learning Vector Quantization(LVQ) for Chronic Kidney Disease Classification

2020 ◽  
Vol 9 (2) ◽  
pp. 241
Author(s):  
I Gst Bgs Bayu Adi Pramana ◽  
I Made Widiartha ◽  
Luh Gede Astuti
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Vector Quantization ◽  
Waste Product ◽  
Learning Vector Quantization ◽  
The Body ◽  
Disease Classification ◽  
Optimal Learning ◽  
Fully Functioning

Chronic kidney disease is a disruption in the function of the kidney organs. When the kidneys are no longer fully functioning, the body is filled with water and a waste product called uremia. As a result, the body or legs will experience swelling and feel tired quickly because the body needs clean blood. Therefore, impaired kidney function should not be underestimated because it can be fatal. Researchers have conducted research related to the classification of kidney disease to find out what symptoms can cause kidney disease. One method that can be used for classification is the Learning Vector Quantization (LVQ) method. In this study, the LVQ algorithm was applied to classify chronic kidney disease. From the research results, the highest accuracy is 81.667% with the optimal learning rate is 0.002.

scholarly journals Do routine hospital data accurately record comorbidity in advanced kidney disease populations? A record linkage cohort study

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ailish Nimmo ◽  
Retha Steenkamp ◽  
Rommel Ravanan ◽  
Dominic Taylor
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
International Classification Of Diseases ◽  
Medical Conditions ◽  
Hospital Data ◽  
Full Spectrum ◽  
Predictive Values ◽  
Advanced Chronic Kidney Disease ◽  
Sensitivity Specificity

Abstract Background Routine healthcare datasets capturing clinical and administrative information are increasingly being used to examine health outcomes. The accuracy of such data is not clearly defined. We examine the accuracy of diagnosis recording in individuals with advanced chronic kidney disease using a routine healthcare dataset in England with comparison to information collected by trained research nurses. Methods We linked records from the Access to Transplant and Transplant Outcome Measures study to the Hospital Episode Statistics dataset. International Classification of Diseases (ICD-10) and Office for Population Censuses and Surveys Classification of Interventions and Procedures (OPCS-4) codes were used to identify medical conditions from hospital data. The sensitivity, specificity, positive and negative predictive values were calculated for a range of diagnoses. Results Comorbidity information was available in 96% of individuals prior to starting kidney replacement therapy. There was variation in the accuracy of individual medical conditions identified from the routine healthcare dataset. Sensitivity and positive predictive values ranged from 97.7 and 90.4% for diabetes and 82.6 and 82.9% for ischaemic heart disease to 44.2 and 28.4% for liver disease. Conclusions Routine healthcare datasets accurately capture certain conditions in an advanced chronic kidney disease population. They have potential for use within clinical and epidemiological research studies but are unlikely to be sufficient as a single resource for identifying a full spectrum of comorbidities.

THYROID DYSFUNCTION IN PATIENTS WITH CHRONIC KIDNEY DISEASE: THE STATE OF THE PROBLEM AND THE WAYS OF SOLVING

2018 ◽  
Vol 22 (4) ◽  
pp. 40-49 ◽  
Author(s):  
A. R. Volkova ◽  
O. D. Dygun ◽  
B. G. Lukichev ◽  
S. V. Dora ◽  
O. V. Galkina
Keyword(s):  
Chronic Kidney Disease ◽  
Thyroid Gland ◽  
Kidney Disease ◽  
Thyroid Hormones ◽  
Thyroid Function ◽  
Thyroid Dysfunction ◽  
The Body ◽  
Type I ◽  
Low T3 ◽  
Iodine Excretion

Disturbance of the thyroid function is often detected in patients with different profiles. A special feature of patients with chronic kidney  disease is the higher incidence of various thyroid function  disturbances, especially hypothyroidism. It is known that in patients  with chronic kidney disease (CKD) iodine excretion from the body is  violated, since normally 90% of iodine is excreted in urine.  Accumulation of high concentrations of inorganic iodine leads to the  formation of the Wolf-Chaikoff effect: suppression of iodine  organization in the thyroid gland and disruption of the thyroid  hormones synthesis. Peripheral metabolism of thyroid hormones is  also disturbed, namely, deiodinase type I activity is suppressed and  peripheral conversion of T4 into T3 is inhibited (so-called low T3  syndrome). Therefore, patients with CKD are often diagnosed with  hypothyroidism, and the origin of hypothyroidism is not always  associated with the outcome of autoimmune thyroiditis. The article  presents an overview of a large number of population studies of  thyroid gland dysfunction in patients with CKD, as well as  experimental data specifying the pathogenetic mechanisms of  thyroid dysfunction in patients with CKD. Therapeutic tactics are still  not regulated. However, in a number of studies, replacement therapy with thyroid hormones in patients with CKD had some advantages.

Clinical classification of hyperuricemia in patients with chronic kidney disease

2021 ◽  
Author(s):  
Fengqin Li ◽  
Hui Guo ◽  
Jianan Zou ◽  
Chensheng Fu ◽  
Song Liu ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Clinical Classification

scholarly journals Classification of SSVEP-based BCIs using Genetic Algorithm

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hamideh Soltani ◽  
Zahra Einalou ◽  
Mehrdad Dadgostar ◽  
Keivan Maghooli
Keyword(s):  
Genetic Algorithm ◽  
Dimension Reduction ◽  
Classification Accuracy ◽  
Bayesian Method ◽  
Computer Interface ◽  
Support Vector ◽  
Svm Classifier ◽  
Effective Dimension ◽  
Effective Dimension Reduction

AbstractBrain computer interface (BCI) systems have been regarded as a new way of communication for humans. In this research, common methods such as wavelet transform are applied in order to extract features. However, genetic algorithm (GA), as an evolutionary method, is used to select features. Finally, classification was done using the two approaches support vector machine (SVM) and Bayesian method. Five features were selected and the accuracy of Bayesian classification was measured to be 80% with dimension reduction. Ultimately, the classification accuracy reached 90.4% using SVM classifier. The results of the study indicate a better feature selection and the effective dimension reduction of these features, as well as a higher percentage of classification accuracy in comparison with other studies.

scholarly journals Classification of Chronic Kidney Disease Biomarkers to Predict Coronary Artery Calcium

2012 ◽  
Vol 36 (1) ◽  
pp. 26-35 ◽  
Author(s):  
Sterling McPherson ◽  
Celestina Barbosa-Leiker ◽  
Robert Short ◽  
Katherine R. Tuttle
Keyword(s):  
Chronic Kidney Disease ◽  
Coronary Artery ◽  
Kidney Disease ◽  
Coronary Artery Calcium ◽  
Disease Biomarkers

Analysing Temporal Effects on Classification of SAR and Optical Images

2021 ◽  
Author(s):  
Ahmet Batuhan Polat ◽  
Ozgun Akcay ◽  
Fusun Balik Sanli
Keyword(s):  
Rural Areas ◽  
Classification Accuracy ◽  
Winter Season ◽  
Support Vector ◽  
Classification Algorithms ◽  
Sar Images ◽  
Optical Images ◽  
Object Based ◽  
Training Samples

<p>Obtaining high accuracy in land cover classification is a non-trivial problem in geosciences for monitoring urban and rural areas. In this study, different classification algorithms were tested with different types of data, and besides the effects of seasonal changes on these classification algorithms and the evaluation of the data used are investigated. In addition, the effect of increasing classification training samples on classification accuracy has been revealed as a result of the study. Sentinel-1 Synthetic Aperture Radar (SAR) images and Sentinel-2 multispectral optical images were used as datasets. Object-based approach was used for the classification of various fused image combinations. The classification algorithms Support Vector Machines (SVM), Random Forest (RF) and K-Nearest Neighborhood (kNN) methods were used for this process. In addition, Normalized Difference Vegetation Index (NDVI) was examined separately to define the exact contribution to the classification accuracy.  As a result, the overall accuracies were compared by classifying the fused data generated by combining optical and SAR images. It has been determined that the increase in the number of training samples improve the classification accuracy. Moreover, it was determined that the object-based classification obtained from single SAR imagery produced the lowest classification accuracy among the used different dataset combinations in this study. In addition, it has been shown that NDVI data does not increase the accuracy of the classification in the winter season as the trees shed their leaves due to climate conditions.</p>

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