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Heart Failure Clinical Records Dataset
Public Health & Epidemiology
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About
Consists of 12 clinical features relevant to predicting mortality in heart failure patients. Given that cardiovascular diseases (CVDs) are the leading cause of death globally, this dataset offers essential insights into the factors contributing to mortality among heart failure patients.
Features:
- Age: Age of the patient (years).
- Anaemia: Indicator of whether the patient has anaemia (1 = yes, 0 = no).
- Creatinine Phosphokinase (CPK): Level of the CPK enzyme in the blood (mcg/L).
- Diabetes: Indicator of whether the patient has diabetes (1 = yes, 0 = no).
- Ejection Fraction: Percentage of blood leaving the heart at each contraction (percent).
- High Blood Pressure: Indicator of high blood pressure (1 = yes, 0 = no).
- Platelets: Platelet count in the blood (kiloplatelets/mL).
- Serum Creatinine: Level of serum creatinine in the blood (mg/dL).
- Serum Sodium: Level of serum sodium in the blood (mEq/L).
- Sex: Gender of the patient (1 = male, 0 = female).
- Smoking: Indicator of whether the patient smokes (1 = yes, 0 = no).
- Time: Follow-up period in days.
Usage:
This dataset can be utilized for:
- Predictive modeling to estimate mortality risk in heart failure patients.
- Clinical research on the impact of various factors (like anaemia, diabetes, and ejection fraction) on heart failure mortality.
- Machine learning projects in healthcare for building models that aid in early intervention for at-risk patients.
Coverage:
The dataset includes patient data from multiple regions and covers key clinical features that are known to influence heart failure outcomes.
License:
CC-BY (Attribution Required)
Who can use it:
This dataset is intended for healthcare data scientists, clinical researchers, and machine learning practitioners interested in cardiovascular risk modeling.
How to use it:
- Apply classification models to predict mortality based on clinical features.
- Analyze the significance of various health indicators (e.g., creatinine levels, ejection fraction) in relation to heart failure outcomes.
- Integrate predictive models into clinical decision-making tools to support early intervention strategies.
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