Extraction of action rules for chronic kidney disease using Naïve bayes classifier

Chronic kidney disease (CKD), also known as chronic renal disease, which is progressive loss in kidney function over a period of months or years. It is defined by the presence of kidney damage or decreased glomerular filtration rate (GFR). The estimated prevalence of CKD is about 9–13% in the general adult population. Individuals with CKD have a far greater likelihood of cardiovascular death than progression to end-stage renal disease. CKD is more prevalent in patients with CVD or with CVD related risk factors, such as hypertension, diabetes mellitus, dyslipidemia, and metabolic syndrome In proposed work, we are not only extracting action rules based on stages but also predicting CKD by using naïve bayes with OneR attribute selector which helps to prevent the advancing of chronic renal disease to further stages.

[1]  Hongwei Wang,et al.  Sentimental feature selection for sentiment analysis of Chinese online reviews , 2015, International Journal of Machine Learning and Cybernetics.

[2]  A. Dubey.  A Classification of CKD Cases Using MultiVariate K-Means Clustering , 2015 .

[3]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[4]  Nguyen Thanh Phuong,et al.  Virus diseases risk-factors associated with shrimp farming practices in rice-shrimp and intensive culture systems in Mekong Delta Viet Nam , 2015 .

[5]  Pirooz Shamsinejad,et al.  A Bayesian network approach for causal action rule mining , 2011 .

[6]  Sithu D. Sudarsan,et al.  Role of attributes selection in classification of Chronic Kidney Disease patients , 2015, 2015 International Conference on Computing, Communication and Security (ICCCS).

[7]  Sri Ramakrishna,et al.  FEATURE SELECTION METHODS AND ALGORITHMS , 2011 .

[8]  Poonam Sinha,et al.  Comparative Study of Chronic Kidney Disease Prediction using KNN and SVM , 2015 .

[9]  Manoj,et al.  Fused Features Classification for the Effective Prediction of Chronic Kidney Disease , 2016 .

[10]  L. Ladha,et al.  FEATURE SELECTION METHODS AND ALGORITHMS , 2011 .

[11]  Christopher H Schmid,et al.  Evaluating the performance of equations for estimating glomerular filtration rate. , 2008, Journal of nephrology.

[12]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[13]  Vikas D. Hajare,et al.  Analysis of ultrasound images for identification of Chronic Kidney Disease stages , 2014, 2014 First International Conference on Networks & Soft Computing (ICNSC2014).

[14]  L. J. Rubini,et al.  Generating comparative analysis of early stage prediction of Chronic Kidney Disease , 2015 .