Data
chronic-kidney-disease

chronic-kidney-disease

active ARFF Publicly available Visibility: public Uploaded 28-05-2021 by Meilina Reksoprodjo
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Author: L.Jerlin Rubini Source: [UCI](https://archive.ics.uci.edu/ml/datasets/chronic_kidney_disease) - 2015 Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) Chronic_Kidney_Disease Data Set This dataset can be used to predict the chronic kidney disease and it can be collected from the hospital nearly 2 months of period. ### 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) 4. Albumin(nominal) 5. Sugar(nominal) 6. Red Blood Cells(nominal) 7. Pus Cell (nominal) 8. Pus Cell clumps(nominal) 9. Bacteria(nominal) 10. Blood Glucose Random(numerical) 11.Blood Urea(numerical) 12. Serum Creatinine(numerical) 13. Sodium(numerical) 14. Potassium(numerical) 15. Hemoglobin(numerical) 16.Packed Cell Volume(numerical) 17. White Blood Cell Count(numerical) 18. Red Blood Cell Count(numerical) 19. Hypertension(nominal) 20. Diabetes Mellitus(nominal) 21. Coronary Artery Disease(nominal) 22. Appetite(nominal) 23. Pedal Edema(nominal) 24. Anemia(nominal) 25. Class (nominal)

26 features

idnumeric400 unique values
0 missing
agenumeric76 unique values
9 missing
bpnumeric10 unique values
12 missing
sgnumeric5 unique values
47 missing
alnumeric6 unique values
46 missing
sunumeric6 unique values
49 missing
rbcstring2 unique values
152 missing
pcstring2 unique values
65 missing
pccstring2 unique values
4 missing
bastring2 unique values
4 missing
bgrnumeric146 unique values
44 missing
bunumeric118 unique values
19 missing
scnumeric84 unique values
17 missing
sodnumeric34 unique values
87 missing
potnumeric40 unique values
88 missing
hemonumeric115 unique values
52 missing
pcvstring44 unique values
70 missing
wcstring92 unique values
105 missing
rcstring49 unique values
130 missing
htnstring2 unique values
2 missing
dmstring5 unique values
2 missing
cadstring3 unique values
2 missing
appetstring2 unique values
1 missing
pestring2 unique values
1 missing
anestring2 unique values
1 missing
classificationstring3 unique values
0 missing

19 properties

400
Number of instances (rows) of the dataset.
26
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
1009
Number of missing values in the dataset.
242
Number of instances with at least one value missing.
12
Number of numeric attributes.
0
Number of nominal attributes.
0.07
Number of attributes divided by the number of instances.
46.15
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
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.
0
Number of binary attributes.
0
Percentage of binary attributes.
60.5
Percentage of instances having missing values.
Average class difference between consecutive instances.
9.7
Percentage of missing values.

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