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Indian-Liver-Patient-Records

Indian-Liver-Patient-Records

active ARFF CC0: Public Domain Visibility: public Uploaded 24-03-2022 by Stewart
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Context Patients with Liver disease have been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles and drugs. This dataset was used to evaluate prediction algorithms in an effort to reduce burden on doctors. Content This data set contains 416 liver patient records and 167 non liver patient records collected from North East of Andhra Pradesh, India. The "Dataset" column is a class label used to divide groups into liver patient (liver disease) or not (no disease). This data set contains 441 male patient records and 142 female patient records. Any patient whose age exceeded 89 is listed as being of age "90". Columns: Age of the patient Gender of the patient Total Bilirubin Direct Bilirubin Alkaline Phosphotase Alamine Aminotransferase Aspartate Aminotransferase Total Protiens Albumin Albumin and Globulin Ratio Dataset: field used to split the data into two sets (patient with liver disease, or no disease) Acknowledgements This dataset was downloaded from the UCI ML Repository: Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. Inspiration Use these patient records to determine which patients have liver disease and which ones do not.

11 features

Dataset (target)numeric2 unique values
0 missing
Agenumeric72 unique values
0 missing
Genderstring2 unique values
0 missing
Total_Bilirubinnumeric113 unique values
0 missing
Direct_Bilirubinnumeric80 unique values
0 missing
Alkaline_Phosphotasenumeric263 unique values
0 missing
Alamine_Aminotransferasenumeric152 unique values
0 missing
Aspartate_Aminotransferasenumeric177 unique values
0 missing
Total_Protiensnumeric58 unique values
0 missing
Albuminnumeric40 unique values
0 missing
Albumin_and_Globulin_Rationumeric69 unique values
4 missing

19 properties

583
Number of instances (rows) of the dataset.
11
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
4
Number of missing values in the dataset.
4
Number of instances with at least one value missing.
10
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of nominal attributes.
0.61
Average class difference between consecutive instances.
90.91
Percentage of numeric attributes.
0.06
Percentage of missing values.
0.69
Percentage of instances having missing values.
0
Percentage of binary attributes.
0
Number of binary attributes.
Number of instances belonging to the least frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the most frequent class.
0.02
Number of attributes divided by the number of instances.

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