Predicting and staging chronic kidney disease of diabetes (Type-2) patient using machine learning algorithms
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.
Kidney Disease Staging, Cross-Validation, morbidity and mortality, Albuminuria, Proteinuria
Setu Basak, Md. Mahbub Alam, Aniruddha Rakshit, Ahmed Al Marouf, Anup Majumder