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chronic-kidney-disease

chronic-kidney-disease

active ARFF CC0: Public Domain Visibility: public Uploaded 24-03-2022 by Stewart
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Context This dataset is originally from UCI Machine Learning Repository. The objective of the dataset is to diagnostically predict whether a patient is having chronic kidney disease or not, based on certain diagnostic measurements included in the dataset. Content The datasets consists of several medical predictor variables and one target variable, Class. Predictor variables includes Blood Pressure(Bp), Albumin(Al), etc. Inspiration Can you build a machine learning model to accurately predict whether or not the patients in the dataset have chronic kidney disease or not?

14 features

Class (target)numeric2 unique values
0 missing
Bpnumeric11 unique values
0 missing
Sgnumeric5 unique values
0 missing
Alnumeric6 unique values
0 missing
Sunumeric6 unique values
0 missing
Rbcnumeric2 unique values
0 missing
Bunumeric118 unique values
0 missing
Scnumeric85 unique values
0 missing
Sodnumeric35 unique values
0 missing
Potnumeric41 unique values
0 missing
Hemonumeric116 unique values
0 missing
Wbccnumeric90 unique values
0 missing
Rbccnumeric46 unique values
0 missing
Htnnumeric3 unique values
0 missing
Show all 14 features

19 properties

NumberOfInstances
400
Number of instances (rows) of the dataset.
NumberOfFeatures
14
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
14
Number of numeric attributes.
NumberOfSymbolicFeatures
0
Number of nominal attributes.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
AutoCorrelation
1
Average class difference between consecutive instances.
PercentageOfNumericFeatures
100
Percentage of numeric attributes.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
NumberOfBinaryFeatures
0
Number of binary attributes.
MinorityClassSize
Number of instances belonging to the least frequent class.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
Dimensionality
0.04
Number of attributes divided by the number of instances.
Show all 19 properties

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