Heart Failure Clinical Records
Donated on 2/4/2020
This dataset contains the medical records of 299 patients who had heart failure, collected during their follow-up period, where each patient profile has 13 clinical features.
Dataset Characteristics
Multivariate
Subject Area
Health and Medicine
Associated Tasks
Classification, Regression, Clustering
Feature Type
Integer, Real
# Instances
299
# Features
12
Dataset Information
Additional Information
A detailed description of the dataset can be found in the Dataset section of the following paper: Davide Chicco, Giuseppe Jurman: "Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone". BMC Medical Informatics and Decision Making 20, 16 (2020). https://doi.org/10.1186/s12911-020-1023-5
Has Missing Values?
No
Introductory Paper
By D. Chicco, Giuseppe Jurman. 2020
Published in BMC Medical Informatics and Decision Making
Variables Table
| Variable Name | Role | Type | Demographic | Description | Units | Missing Values |
|---|---|---|---|---|---|---|
| age | Feature | Integer | Age | age of the patient | years | no |
| anaemia | Feature | Binary | decrease of red blood cells or hemoglobin | no | ||
| creatinine_phosphokinase | Feature | Integer | level of the CPK enzyme in the blood | mcg/L | no | |
| diabetes | Feature | Binary | if the patient has diabetes | no | ||
| ejection_fraction | Feature | Integer | percentage of blood leaving the heart at each contraction | % | no | |
| high_blood_pressure | Feature | Binary | if the patient has hypertension | no | ||
| platelets | Feature | Continuous | platelets in the blood | kiloplatelets/mL | no | |
| serum_creatinine | Feature | Continuous | level of serum creatinine in the blood | mg/dL | no | |
| serum_sodium | Feature | Integer | level of serum sodium in the blood | mEq/L | no | |
| sex | Feature | Binary | Sex | woman or man | no |
0 to 10 of 13
Additional Variable Information
Thirteen (13) clinical features: - age: age of the patient (years) - anaemia: decrease of red blood cells or hemoglobin (boolean) - creatinine phosphokinase (CPK): level of the CPK enzyme in the blood (mcg/L) - diabetes: if the patient has diabetes (boolean) - ejection fraction: percentage of blood leaving the heart at each contraction (percentage) - high blood pressure: if the patient has hypertension (boolean) - platelets: platelets in the blood (kiloplatelets/mL) - sex: woman or man (binary) - serum creatinine: level of serum creatinine in the blood (mg/dL) - serum sodium: level of serum sodium in the blood (mEq/L) - smoking: if the patient smokes or not (boolean) - time: follow-up period (days) - [target] death event: if the patient died during the follow-up period (boolean) For more information, please check Table 1, Table 2, and Table 3 of the following paper: Davide Chicco, Giuseppe Jurman: "Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone". BMC Medical Informatics and Decision Making 20, 16 (2020). https://doi.org/10.1186/s12911-020-1023-5
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset heart_failure_clinical_records = fetch_ucirepo(id=519) # data (as pandas dataframes) X = heart_failure_clinical_records.data.features y = heart_failure_clinical_records.data.targets # metadata print(heart_failure_clinical_records.metadata) # variable information print(heart_failure_clinical_records.variables)
Heart Failure Clinical Records. (2020). UCI Machine Learning Repository. https://doi.org/10.24432/C5Z89R.
@misc{misc_heart_failure_clinical_records_519,
title = {{Heart Failure Clinical Records}},
year = {2020},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5Z89R}
}DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.