Data
ChonicKidneyDisease

ChonicKidneyDisease

active ARFF Public Domain (CC0) Visibility: public Uploaded 28-10-2021 by Jonathan Manteo
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  • chronic kidney disease machine learning ML survival analysis
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We use the following representation to collect the dataset age - age bp - blood pressure sg - specific gravity al - albumin su - sugar rbc - red blood cells pc - pus cell pcc - pus cell clumps ba - bacteria bgr - blood glucose random bu - blood urea sc - serum creatinine sod - sodium pot - potassium hemo - hemoglobin pcv - packed cell volume wc - white blood cell count rc - red blood cell count htn - hypertension dm - diabetes mellitus cad - coronary artery disease appet - appetite pe - pedal edema ane - anemia class - class Attribute Information: We use 24 + class = 25 ( 11 numeric ,14 nominal) 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)

28 features

Idnumeric250 unique values
0 missing
Agenumeric70 unique values
0 missing
Sexnominal2 unique values
0 missing
Blood_Pressurenumeric11 unique values
0 missing
Specific_Gravitynumeric5 unique values
0 missing
Albuminnumeric5 unique values
0 missing
Sugarnumeric6 unique values
0 missing
RBCnominal2 unique values
0 missing
PCnominal2 unique values
0 missing
PCCnominal2 unique values
0 missing
Bacterianominal2 unique values
0 missing
Blood_Gluc_randnumeric136 unique values
0 missing
Blood_Ureanumeric118 unique values
0 missing
Creatininenumeric51 unique values
0 missing
Sodiumnumeric33 unique values
0 missing
Potassiumnumeric41 unique values
0 missing
Hemoglobinnumeric84 unique values
0 missing
Packed_Cell_Volumenumeric38 unique values
0 missing
Wbc_cntnumeric82 unique values
0 missing
Rbc_cntnumeric7 unique values
0 missing
ACRnumeric96 unique values
0 missing
Hypertensionnominal2 unique values
0 missing
Diabetesnominal3 unique values
0 missing
CADnominal2 unique values
0 missing
Apetitenominal2 unique values
0 missing
Pedal_edemanominal2 unique values
0 missing
Anemianominal2 unique values
0 missing
Survivalnumeric5 unique values
0 missing

19 properties

250
Number of instances (rows) of the dataset.
28
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
17
Number of numeric attributes.
11
Number of nominal attributes.
0.11
Number of attributes divided by the number of instances.
60.71
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
39.29
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
10
Number of binary attributes.
35.71
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

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