Data
medical_charges_nominal

medical_charges_nominal

active ARFF Publicly available Visibility: public Uploaded 25-06-2020 by Marcos de Paula Bueno
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Centers for Medicare & Medicaid Services Source: [original](https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/Downloads/Inpatient_Data_2011_CSV.zip) - 14-08-2018 Please cite: Patricio Cerda, Gael Varoquaux, Balazs Kegl. Similarity encoding for learning with dirty categorical variables. 2018. Machine Learning journal, Springer. The Inpatient Utilization and Payment Public Use File (Inpatient PUF) provides information on inpatient discharges for Medicare fee-for-service beneficiaries. The Inpatient PUF includes information on utilization, payment (total payment and Medicare payment), and hospital-specificcharges for the more than 3,000 U.S. hospitals that receive Medicare Inpatient Prospective Payment System (IPPS) payments. The PUF is organized by hospital and Medicare Severity Diagnosis Related Group (MS-DRG) and covers Fiscal Year (FY) 2011 through FY 2016.

12 features

Average_Total_Payments (target)numeric154891 unique values
0 missing
DRG_Definitionnominal100 unique values
0 missing
Provider_Idnominal3337 unique values
0 missing
Provider_Namenominal3201 unique values
0 missing
Provider_Street_Addressnominal3326 unique values
0 missing
Provider_Citynominal1977 unique values
0 missing
Provider_Statenominal51 unique values
0 missing
Provider_Zip_Codenominal3053 unique values
0 missing
Hospital_Referral_Region_(HRR)_Descriptionnominal306 unique values
0 missing
Total_Dischargesnumeric642 unique values
0 missing
Average_Covered_Chargesnumeric161985 unique values
0 missing
Average_Medicare_Paymentsnumeric157817 unique values
0 missing

19 properties

163065
Number of instances (rows) of the dataset.
12
Number of attributes (columns) of the dataset.
0
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.
4
Number of numeric attributes.
8
Number of nominal attributes.
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.
0
Percentage of instances having missing values.
0
Percentage of missing values.
-1978.31
Average class difference between consecutive instances.
33.33
Percentage of numeric attributes.
0
Number of attributes divided by the number of instances.
66.67
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.

7 tasks

0 runs - estimation_procedure: 33% Holdout set - target_feature: Average_Total_Payments
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task