I'm doing a research to correctly identify people have a higher probability of getting affected by chronic kidney disease. In order to do that I need a data set of people who already have ckd and not. (nearly 1000 records) 1.Age(numerical) age in years 2.Blood Pressure(numerical) bp in mm/Hg 3.Specific Gravity(nominal) sg - (1.005,1.010,1.015,1.020,1.025) 4.Albumin(nominal) al - (0,1,2,3,4,5) 5.Sugar(nominal) su - (0,1,2,3,4,5) 6.Red Blood Cells(nominal) rbc - (normal,abnormal) 7.Pus Cell (nominal) pc - (normal,abnormal) 8.Pus Cell clumps(nominal) pcc - (present,notpresent) 9.Bacteria(nominal) ba - (present,notpresent) 10.Blood Glucose Random(numerical) bgr in mgs/dl 11.Blood Urea(numerical) bu in mgs/dl 12.Serum Creatinine(numerical) sc in mgs/dl 13.Sodium(numerical) sod in mEq/L 14.Potassium(numerical) pot in mEq/L 15.Hemoglobin(numerical) hemo in gms 16.Packed Cell Volume(numerical) 17.White Blood Cell Count(numerical) wc in cells/cumm 18.Red Blood Cell Count(numerical) rc in millions/cmm 19.Hypertension(nominal) htn - (yes,no) 20.Diabetes Mellitus(nominal) dm - (yes,no) 21.Coronary Artery Disease(nominal) cad - (yes,no) 22.Appetite(nominal) appet - (good,poor) 23.Pedal Edema(nominal) pe - (yes,no) 24.Anemia(nominal) ane - (yes,no) 25.Class (nominal) class - (ckd,notckd) Class is mainly used for classification. I have to classify people and correctly identify whether they have the disease or not.

The data is essential to model the problem so that I can do a better classification. This would also help the people to identify chronic kidney disease at an earlier level so that they get an opportunity to get early medical attention.

A larger data set of nearly 1000 records would give a better chance to model a better mechanism or propose a more accurate algorithm to detect ckd.