scholarly journals Assessment of Chronic Kidney Disease using Classification Algorithms

2019 ◽  
Vol 8 (4) ◽  
pp. 10385-10389
Keyword(s):  
Kidney Disease ◽  
Naive Bayes ◽  
Early Stage ◽  
Classification Performance ◽  
Naïve Bayes ◽  
Conditional Independence Assumption ◽  
Probabilistic Classifier ◽  
Accuracy Difference ◽  
Stage Renal Disease ◽  
End Stage

Kidney Disease (CKD) implies the condition of kidney risk which may even get worse by time and by referring the factors. If it continues to get worse Dialysis is done and worstcase scenario it may lead to kidney failure (End-Stage Renal Disease). Detection of CKD in an early stage could help in sorting out the complications and damage. In the previous work classification used are SVM and Naïve Bayes, it resulted that the execution time took by Naïve Bayes is minimal compared to SVM, incorrect instances are less for SVM that results in less classification performance of Naïve Bayes, because of slight accuracy difference. It can be rectified by taking a smaller number of attributes. Naïve Bayes is a probabilistic classifier a simple computation by applying Bayes Theorem with a conditional independence assumption. The work mainly results in increasing diagnostic accuracy and decrease diagnosis time, that is the main aim. An attempt is made to develop a model evaluating CKD data gathered from a particular set of people. From the model data, identification can be done. This work has engrossed on developing a system based on classification methods: SVM, Naïve Bayes, KNN.

scholarly journals Chronic Kidney Disease using Machine Learning Techniques

2020 ◽  
Vol 9 (7) ◽  
pp. 683-`686
Keyword(s):  
Kidney Disease ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Disease Area ◽  
Course Of Action ◽  
Learning Techniques ◽  
Probabilistic Classifier ◽  
Stage Renal Disease ◽  
End Stage

Interminable Kidney Disease (CKD) proposes the realm of kidney chance which may even crumble by means of time and through implying the factors. If it continues finishing all the more dreadful Dialysis is and most desperate conclusive outcomes believable it'd flash off kidney misery (End-Stage Renal Disease). Area of CKD in a starting period should help in filtering by means of the complexities and harm.In the pastwork portrayal applied are SVM and Naïve Bayes, it happened that the execution time took by methods for Naïve Bayes is irrelevant appeared differently in relation to SVM, confused events are substantially less with SVM that results in less request execution of Naïve Bayes, inferable from gentle exactness distinction. It can be corrected by methods for taking less improvements. Unsuspecting Bayes is a probabilistic classifier a fundamental count by utilizing Bayes Theorem with a prohibitive independence supposition. The artistic creations for the most segment brings around growing symptomatic exactness and decrease commitment time, this is the guideline factor. An undertaking is made to develop a form evaluating CKD data collected from a particular course of action of people. From the model data, recognizing verification should be conceivable. This work has enchanted on developing up a system relying upon gathering procedures: SVM, Naïve Bayes, glomerular filtration rate (GFR) is the best pointer of how well the kidneys are working.CKD has got no cure but it can be treated based on symptoms to reduce complicationsand

A short guideline on chronic kidney disease for medical laboratory practice / Kronik böbrek hastalığında tıbbi laboratuvar uygulamaları için kısa kılavuz

2016 ◽  
Vol 41 (4) ◽  
Author(s):  
Sedat Abuşoğlu ◽  
İlknur Aydın ◽  
Funda Bakar ◽  
Tan Bekdemir ◽  
Özlem Gülbahar ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Cystatin C ◽  
Early Stage ◽  
Pediatric Population ◽  
Adult Population ◽  
Isotope Dilution Mass Spectrometry ◽  
Medical Laboratory ◽  
Stage Renal Disease ◽  
End Stage

AbstractChronic kidney disease (CKD) is asymptomatic in the early stage. Kidney function might be lost 90% when the symptoms are overt. However, in case of early detection, progression of the disease can be prevented or delayed. If not detected it results in end stage renal disease. Therefore, the level of awareness about CKD should be increased. The role of medical laboratory is utmost important for the diagnosis and staging of CKD. In this paper, the main tasks of the laboratory specialists are described and the outlines are as follows.• Creatinine assays should be traceable to internationally recognised reference materials and methods, specifically isotope dilution mass spectrometry.• When reporting the creatinine result, eGFR should also be reported in adult (>18 years) population. A warning expression should be included in the report form if eGFR result is <60 mL/min/1.73 m• eGFR values should be expressed quantitatively up to 90 mL/min/1.73 m• eGFR equations of the adult population should not be used for pediatric population. Different equations utilizing also patient height should be used. The enzymatic creatinine assay should be preferred. eGFR based on cystatin C can be used for confirmation in the pediatric population.• Cystatin C measurements, at least when eGFR based on creatinine is not reliable and for confirmation should be encouraged.• Proteinuria or albuminuria values should be measured in spot samples and reported in proportion to creatinine.

EXPLORING CONDITIONS FOR THE OPTIMALITY OF NAÏVE BAYES

2005 ◽  
Vol 19 (02) ◽  
pp. 183-198 ◽  
Author(s):  
HARRY ZHANG
Keyword(s):  
Conditional Independence ◽  
Naive Bayes ◽  
Inductive Learning ◽  
Learning Algorithms ◽  
Classification Performance ◽  
Naïve Bayes ◽  
Necessary Condition ◽  
Conditional Independence Assumption ◽  
Good Classification Performance ◽  
Open Question

Naïve Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. Its competitive performance in classification is surprising, because the conditional independence assumption on which it is based is rarely true in real-world applications. An open question is: what is the true reason for the surprisingly good performance of Naïve Bayes in classification? In this paper, we propose a novel explanation for the good classification performance of Naïve Bayes. We show that, essentially, dependence distribution plays a crucial role. Here dependence distribution means how the local dependence of an attribute distributes in each class, evenly or unevenly, and how the local dependences of all attributes work together, consistently (supporting a certain classification) or inconsistently (canceling each other out). Specifically, we show that no matter how strong the dependences among attributes are, Naïve Bayes can still be optimal if the dependences distribute evenly in classes, or if the dependences cancel each other out. We propose and prove a sufficient and necessary condition for the optimality of Naïve Bayes. Further, we investigate the optimality of Naïve Bayes under the Gaussian distribution. We present and prove a sufficient condition for the optimality of Naïve Bayes, in which the dependences among attributes exist. This provides evidence that dependences may cancel each other out. Our theoretic analysis can be used in designing learning algorithms. In fact, a major class of learning algorithms for Bayesian networks are conditional independence-based (or CI-based), which are essentially based on dependence. We design a dependence distribution-based algorithm by extending the ChowLiu algorithm, a widely used CI based algorithm. Our experiments show that the new algorithm outperforms the ChowLiu algorithm, which also provides empirical evidence to support our new explanation.

scholarly journals A Novel Hybrid Approach: Instance Weighted Hidden Naive Bayes

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2982
Author(s):  
Liangjun Yu ◽  
Shengfeng Gan ◽  
Yu Chen ◽  
Dechun Luo
Keyword(s):  
Structure Learning ◽  
Naive Bayes ◽  
Hybrid Approach ◽  
Classification Performance ◽  
Naïve Bayes ◽  
Network Algorithms ◽  
Probability Estimates ◽  
Conditional Independence Assumption ◽  
Instance Weighting ◽  
Hidden Naive Bayes

Naive Bayes (NB) is easy to construct but surprisingly effective, and it is one of the top ten classification algorithms in data mining. The conditional independence assumption of NB ignores the dependency between attributes, so its probability estimates are often suboptimal. Hidden naive Bayes (HNB) adds a hidden parent to each attribute, which can reflect dependencies from all the other attributes. Compared with other Bayesian network algorithms, it offers significant improvements in classification performance and avoids structure learning. However, the assumption that HNB regards each instance equivalent in terms of probability estimation is not always true in real-world applications. In order to reflect different influences of different instances in HNB, the HNB model is modified into the improved HNB model. The novel hybrid approach called instance weighted hidden naive Bayes (IWHNB) is proposed in this paper. IWHNB combines instance weighting with the improved HNB model into one uniform framework. Instance weights are incorporated into the improved HNB model to calculate probability estimates in IWHNB. Extensive experimental results show that IWHNB obtains significant improvements in classification performance compared with NB, HNB and other state-of-the-art competitors. Meanwhile, IWHNB maintains the low time complexity that characterizes HNB.

scholarly journals Investigation of the Impact of Endodontic Therapy on Survival among Dialysis Patients in Taiwan: A Nationwide Population-Based Cohort Study

2021 ◽  
Vol 18 (1) ◽  
pp. 326
Author(s):  
Chih-Chien Chiu ◽  
Ya-Chieh Chang ◽  
Ren-Yeong Huang ◽  
Jenq-Shyong Chan ◽  
Chi-Hsiang Chung ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Renal Disease ◽  
End Stage Renal Disease ◽  
Root Canal ◽  
Dialysis Patients ◽  
Root Canal Therapy ◽  
Risk Of Death ◽  
Stage Renal Disease ◽  
End Stage

Objectives Dental problems occur widely in patients with chronic kidney disease (CKD) and may increase comorbidities. Root canal therapy (RCT) is a common procedure for advanced decayed caries with pulp inflammation and root canals. However, end-stage renal disease (ESRD) patients are considered to have a higher risk of potentially life-threatening infections after treatment and might fail to receive satisfactory dental care such as RCT. We investigated whether appropriate intervention for dental problems had a potential impact among dialysis patients. Design Men and women who began maintenance dialysis (hemodialysis or peritoneal dialysis) between January 1, 2000, and December 31, 2015, in Taiwan (total 12,454 patients) were enrolled in this study. Participants were followed up from the first reported dialysis date to the date of death or end of dialysis by December 31, 2015. Setting Data collection was conducted in Taiwan. Results A total of 2633 and 9821 patients were classified into the RCT and non-RCT groups, respectively. From the data of Taiwan’s National Health Insurance, a total of 5,092,734 teeth received RCT from 2000 to 2015. Then, a total of 12,454 patients were followed within the 16 years, and 4030 patients passed away. The results showed that members of the non-RCT group (34.93%) had a higher mortality rate than those of the RCT group (22.79%; p = 0.001). The multivariate-adjusted hazard ratio for the risk of death was 0.69 (RCT vs. non-RCT; p = 0.001). Conclusions This study suggested that patients who had received RCT had a relatively lower risk of death among dialysis patients. Infectious diseases had a significant role in mortality among dialysis patients with non-RCT. Appropriate interventions for dental problems may increase survival among dialysis patients. Abbreviations: CKD = chronic kidney disease, ESRD = end-stage renal disease, RCT = root canal therapy.

scholarly journals The Severity of Diabetic Retinopathy Is an Independent Factor for the Progression of Diabetic Nephropathy

2020 ◽  
Vol 10 (1) ◽  
pp. 3
Author(s):  
Shi-Chue Hsing ◽  
Chia-Cheng Lee ◽  
Chin Lin ◽  
Jiann-Torng Chen ◽  
Yi-Hao Chen ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Kidney Disease ◽  
Renal Disease ◽  
Disease Progression ◽  
Renal Disease Progression ◽  
Hazard Ratios ◽  
Stage Renal Disease ◽  
End Stage

(1) Background: It has rarely been studied whether the severity of diabetic retinopathy (DR) could influence renal disease progression in end-stage renal disease (ESRD) and chronic kidney disease (CKD) in patients with type 2 diabetes. The aim of this study was to evaluate renal disease progression in ESRD and CKD according to DR severity in patients with type 2 diabetes. (2) Methods: We included 1329 patients and divided the cohort into two end-points. The first was to trace the incidence of ESRD in all enrolled participants and the other was to follow their progression to CKD. (3) Results: Significantly higher crude hazard ratios (HRs) of ESRD incidence in all enrolled participants were noted, and this ratio increased in a stepwise fashion. However, after adjustment, DR severity was not associated with ESRD events. Therefore, a subgroup of 841 patients without CKD was enrolled to track their progression to CKD. Compared with no diabetic retinopathy, the progression of CKD increased in a stepwise fashion, from mild nonproliferative diabetic retinopathy (NPDR) to moderate NPDR, to severe NPDR and to proliferative diabetic retinopathy (PDR), both in the crude and adjusted models. (4) Conclusions: The severity of retinopathy appeared to be associated with renal lesions and the development of CKD. Our findings suggest that the severity of DR is a risk factor for progression to CKD. Therefore, diabetic retinopathy is useful for prognosticating the clinical course of diabetic kidney disease.

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