scholarly journals Data Mining Techniques to Predict Chronic Kidney Disease

2019 ◽  
pp. 1220-1226
Author(s):  
Golam Murshid ◽  
Thakor Parvez ◽  
Nagani Fezal ◽  
Lakhani Azaz ◽  
Mohammad Asif
Keyword(s):  
United States ◽  
Data Mining ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Prediction Models ◽  
Naive Bayes ◽  
Predictive Analytics ◽  
The United States ◽  
Naïve Bayes ◽  
Increased Risk

<p>Chronic Kidney Disease incorporates the state where the kidneys fail to function and reduce the potential to keep a person suffering from the disease healthy. When the condition of the kidneys gets worse, the wastes in the blood are formed in high level. Data mining has been a present pattern for accomplishing analytic outcomes. Colossal measure of un-mined data is gathered by the human services industry so as to find concealed data for powerful analysis and basic leadership. Data mining is the way towards extricating concealed data from gigantic datasets. The goal of our paper is to anticipate CKD utilizing the classification strategy Naïve Bayes. The phases of CKD are anticipated in the light of Glomerular Filtration Rate (GFR). Chronic Kidney Disease (CKD) is one of the most widespread illnesses in the United States. Recent statistics show that twenty-six million adults in the United States have CKD and million others are at increased risk. Clinical diagnosis of CKD is based on blood and urine tests as well as removing a sample of kidney tissue for testing. Early diagnosis and detection of kidney disease is important to help stop the progression to kidney failure. Data mining and analytics techniques can be used for predicting CKD by utilizing historical patient’s data and diagnosis records. In this research, predictive analytics techniques such as Decision Trees, Logistic Regression, Naive Bayes, and Artificial Neural Networks are used for predicting CKD. Pre-processing of the data is performed to impute any missing data and identify the variables that should be considered in the prediction models. The different predictive analytics models are assessed and compared based on accuracy of prediction. The study provides a decision support tool that can help in the diagnosis of CKD.</p>

scholarly journals Sickle Cell Trait and Renal Function in Hispanics in the United States: the Northern Manhattan Study

2017 ◽  
Vol 27 (1) ◽  
pp. 11 ◽  
Author(s):  
Nicole D. Dueker ◽  
David Della-Morte ◽  
Tatjana Rundek ◽  
Ralph L. Sacco ◽  
Susan H. Blanton
Keyword(s):  
United States ◽  
Chronic Kidney Disease ◽  
African Americans ◽  
Kidney Disease ◽  
Sickle Cell ◽  
Sickle Cell Trait ◽  
The United States ◽  
Genetic Mutation ◽  
Single Mutation ◽  
Increased Risk

<p class="Pa7">Sickle cell anemia (SCA) is a common hematological disorder among individu­als of African descent in the United States; the disorder results in the production of abnormal hemoglobin. It is caused by homozygosity for a genetic mutation in HBB; rs334. While the presence of a single mutation (sickle cell trait, SCT) has long been considered a benign trait, recent research suggests that SCT is associated with renal dysfunction, including a decrease in estimated glomerular filtration rate (eGFR) and increased risk of chronic kidney disease (CKD) in African Americans. It is currently unknown whether similar associations are observed in Hispanics. Therefore, our study aimed to determine if SCT is associated with mean eGFR and CKD in a sample of 340 Dominican Hispanics from the Northern Manhattan Study. Using regression analyses, we tested rs334 for association with eGFR and CKD, adjusting for age and sex. eGFR was estimated using the Chronic Kidney Disease Epidemiology Collaboration equa­tion and CKD was defined as eGFR &lt; 60 mL/min/1.73 m2. Within our sample, there were 16 individuals with SCT (SCT carriers). We found that SCT carriers had a mean eGFR that was 12.12 mL/min/1.73m2 lower than non-carriers (P=.002). Additionally, SCT carriers had 2.72 times higher odds of CKD compared with non-carriers (P=.09). Taken together, these novel results show that Hispanics with SCT, as found among African Americans with SCT, may also be at increased risk for kidney disease.</p><p class="Pa7"><em>Ethn Dis. </em>2017; 27(1)<strong>:</strong>11-14; doi:10.18865/ed.27.1.11.</p><p class="Pa7"> </p>

scholarly journals Diagnosa Penyakit Ginjal Kronis Menggunakan Metode Klasifikasi Naïve

2020 ◽  
Vol 4 (3) ◽  
pp. 850
Author(s):  
Harmayani Harmayani ◽  
Lamhot Sitorus
Keyword(s):  
United States ◽  
Data Mining ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Body Condition ◽  
The United States ◽  
The Body ◽  
Bayes Classifier ◽  
Classification Approach ◽  
Diagnostic Data

Chronic Kidney Disease is a very dangerous disease that is often not seriously considered with the effects of this disease which leads to death. More than 26 million people in the United States are not aware of their kidney disease, only 8% of them begin to realize the disease, each body must be known early whether or not the body condition / / by diagnosing it, in this study classifications will be carried out on the diagnostic data attributes Chronic kidney disease aims to simplify the process of classifying symptoms and making decisions on the diagnosis of kidney disease, this process is carried out using a data mining classification approach using the Naïve Bayes Classifier method

scholarly journals Prediction of benign and malignant breast cancer using data mining techniques

2018 ◽  
Vol 12 (2) ◽  
pp. 119-126 ◽  
Author(s):  
Vikas Chaurasia ◽  
Saurabh Pal ◽  
BB Tiwari
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Low Income ◽  
Prediction Models ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Low Income Countries ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Rbf Network

Breast cancer is the second most leading cancer occurring in women compared to all other cancers. Around 1.1 million cases were recorded in 2004. Observed rates of this cancer increase with industrialization and urbanization and also with facilities for early detection. It remains much more common in high-income countries but is now increasing rapidly in middle- and low-income countries including within Africa, much of Asia, and Latin America. Breast cancer is fatal in under half of all cases and is the leading cause of death from cancer in women, accounting for 16% of all cancer deaths worldwide. The objective of this research paper is to present a report on breast cancer where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability. We used three popular data mining algorithms (Naïve Bayes, RBF Network, J48) to develop the prediction models using a large dataset (683 breast cancer cases). We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results (based on average accuracy Breast Cancer dataset) indicated that the Naïve Bayes is the best predictor with 97.36% accuracy on the holdout sample (this prediction accuracy is better than any reported in the literature), RBF Network came out to be the second with 96.77% accuracy, J48 came out third with 93.41% accuracy.

scholarly journals Trends in anemia care in non-dialysis-dependent chronic kidney disease (CKD) patients in the United States (2006–2015)

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Haesuk Park ◽  
Xinyue Liu ◽  
Linda Henry ◽  
Jeffrey Harman ◽  
Edward A. Ross
Keyword(s):  
United States ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
The United States

scholarly journals Telomere attrition, kidney function, and prevalent chronic kidney disease in the United States

Oncotarget ◽  
2017 ◽  
Vol 8 (46) ◽  
pp. 80175-80181 ◽  
Author(s):  
Moshen Mazidi ◽  
Peyman Rezaie ◽  
Adriac Covic ◽  
Jolanta Malyszko ◽  
Jacek Rysz ◽  
...  
Keyword(s):  
United States ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Kidney Function ◽  
The United States ◽  
Telomere Attrition

Chronic Kidney Failure and Dialysis

2018 ◽  
Author(s):  
Raghu V Durvasula ◽  
Jonathan Himmelfarb
Keyword(s):  
United States ◽  
Chronic Kidney Disease ◽  
Peritoneal Dialysis ◽  
Kidney Disease ◽  
Renal Disease ◽  
Chronic Renal Disease ◽  
Clinical Manifestations ◽  
Kidney Injury ◽  
The United States ◽  
Clinical Syndrome

Chronic kidney disease (CKD) is a clinical syndrome arising from progressive kidney injury, formerly known as chronic renal failure, chronic renal disease, and chronic renal insufficiency. It is classified into five stages based primarily on glomerular filtration rate (GFR). This article discusses the epidemiology of CKD and end-stage renal disease (ESRD), as well as etiology and genetics, pathophysiology, and pathogenesis. The section on diagnosis looks at clinical manifestations and physical findings, laboratory (and other) tests, imaging studies, and biopsy. A short section on differential diagnosis is followed by a discussion of treatment, including hemodialysis and peritoneal dialysis. Long-term complications of patients on dialysis include cardiovascular disease, renal osteodystrophy, dialysis-related amyloidosis, and acquired cystic disease (renal cell carcinoma). The final section addresses prognosis and socioeconomic burden. Figures include the classification system for CKD, prevalence of CKD in the United States, rising prevalence, risk of, and leading causes of ESRD in the United States, plus the changing prevalence of ESRD over time, clinical manifestations of uremia, and an overview of hemodialysis circuit. Tables look at the burden of CKD relative to other chronic disorders, the specific hereditary causes of kidney disease, and situations when serum creatinine does not accurately predict GFR. Other tables list equations for estimating GFR, the causes of CKD without shrunken kidneys, and clinical features distinguishing chronic kidney disease from acute kidney injury. ESRD and indications for initiation of dialysis are presented, as well as typical composition of dialysate and reasons for failure of peritoneal dialysis. This chapter contains 71 references.

Chronic Kidney Failure and Dialysis

2017 ◽  
Author(s):  
Raghu V Durvasula ◽  
Jonathan Himmelfarb
Keyword(s):  
United States ◽  
Chronic Kidney Disease ◽  
Peritoneal Dialysis ◽  
Kidney Disease ◽  
Renal Disease ◽  
Chronic Renal Disease ◽  
Clinical Manifestations ◽  
Kidney Injury ◽  
The United States ◽  
Clinical Syndrome

Chronic kidney disease (CKD) is a clinical syndrome arising from progressive kidney injury, formerly known as chronic renal failure, chronic renal disease, and chronic renal insufficiency. It is classified into five stages based primarily on glomerular filtration rate (GFR). This article discusses the epidemiology of CKD and end-stage renal disease (ESRD), as well as etiology and genetics, pathophysiology, and pathogenesis. The section on diagnosis looks at clinical manifestations and physical findings, laboratory (and other) tests, imaging studies, and biopsy. A short section on differential diagnosis is followed by a discussion of treatment, including hemodialysis and peritoneal dialysis. Long-term complications of patients on dialysis include cardiovascular disease, renal osteodystrophy, dialysis-related amyloidosis, and acquired cystic disease (renal cell carcinoma). The final section addresses prognosis and socioeconomic burden. Figures include the classification system for CKD, prevalence of CKD in the United States, rising prevalence, risk of, and leading causes of ESRD in the United States, plus the changing prevalence of ESRD over time, clinical manifestations of uremia, and an overview of hemodialysis circuit. Tables look at the burden of CKD relative to other chronic disorders, the specific hereditary causes of kidney disease, and situations when serum creatinine does not accurately predict GFR. Other tables list equations for estimating GFR, the causes of CKD without shrunken kidneys, and clinical features distinguishing chronic kidney disease from acute kidney injury. ESRD and indications for initiation of dialysis are presented, as well as typical composition of dialysate and reasons for failure of peritoneal dialysis. This chapter contains 71 references.

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