A predictive model for kidney failure E-health

Ancy, S.; Cornelius, Kathleen

doi:10.1109/ssps.2017.8071647

Cited by 1 publication

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [5] M. Edhayadharshini have proposed a method to identify CKD from Computer Tomography (CT) abdominal images where they have used feature selection of the segmented image by Gabor-PHOG features. In [13,14,15] authors have discussed the effect of training with the imbalance data. U. N. Dulhare et al [13] ap-plied Sequential minimal optimization, OneR for classification, Naïve Bayes and Instance Based Learning (IBK) for the prediction task.…”

Section: Related Workmentioning

confidence: 99%

Predicting and Staging Chronic Kidney Disease of Diabetes (Type-2) Patient using Machine Learning Algorithms

Basak¹,

Alam²,

Rakshit³

et al. 2019

IJITEE

View full text Add to dashboard Cite

Mortality because of unending kidney disease increments essentially in recent years. Nowadays, about 422 million patients are suffering from diabetes among them around 30 percent of patients with Type 1 (adolescent beginning) diabetes and around 10 to 40 percent of those with Type 2 (grown-up beginning) diabetes in the end will experience the negative impacts of kidney damage. It is evident, that early detection of Chronic Kidney Disease (CKD) can mitigate the level of damage in the adulthood. In this paper, we have presented a comparative analysis based on the performance of five different algorithms-Naive Bayes (NB), In-stance Based Learning (IBK), Random Forest (RF), Decision Stump (DS) and Decision Tree (J48) for predicting CKD of diabetes patients only by urine test. Among all the algorithms the IBK gives the best result. Our comparison of different algorithms will help people with diabetes to find out if they are having CKD or not.

show abstract