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Clinical Records for Heart Failure Analysis
Patient Health Records & Digital Health
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About
Contains clinical features from patients with heart failure, a common event caused by cardiovascular diseases (CVDs). Cardiovascular diseases are the leading cause of death globally. This collection of 12 clinical features can be used to build machine learning models to predict mortality from heart failure. Early detection and management are crucial for individuals with or at high risk of cardiovascular disease, and this data can aid in developing such predictive tools.
Columns
- age: The age of the patient in years.
- anaemia: A boolean value indicating a decrease in red blood cells or haemoglobin.
- creatinine_phosphokinase: The level of the CPK enzyme in the blood, measured in mcg/L.
- diabetes: A boolean value indicating if the patient has diabetes.
- ejection_fraction: The percentage of blood leaving the heart with each contraction.
- high_blood_pressure: A boolean value indicating if the patient has hypertension.
- platelets: The count of platelets in the blood, measured in kiloplatelets/mL.
- serum_creatinine: The level of serum creatinine in the blood, measured in mg/dL.
- serum_sodium: The level of serum sodium in the blood, measured in mEq/L.
- sex: The patient's sex, represented as a binary value (man or woman).
- smoking: A boolean value indicating if the patient is a smoker.
- time: The follow-up period in days.
- DEATH_EVENT: The target variable; a boolean value indicating if the patient died during the follow-up period.
Distribution
- Format: CSV file (
Heart Failure Clinical Records.csv) - Size: 12.54 kB
- Structure: The data consists of 299 records and 13 columns.
Usage
This data is ideal for developing machine learning models aimed at predicting patient mortality due to heart failure. It can be used for academic research, healthcare analytics, and building decision-support systems for clinicians. Key applications include risk stratification for patients with cardiovascular disease and identifying key predictive factors for heart failure outcomes.
Coverage
The data contains clinical records for 299 patients. The time range is captured by the 'time' column, which records the follow-up period in days for each patient. Specific geographic or detailed demographic scope beyond the provided clinical features is not specified.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
- Data Scientists & Machine Learning Engineers: To build and validate predictive models for patient mortality.
- Medical Researchers: To study the relationships between clinical features and heart failure outcomes.
- Healthcare Analysts: To perform statistical analysis on risk factors associated with CVDs.
- Students: To practise data analysis, feature engineering, and machine learning techniques on a real-world medical dataset.
Dataset Name Suggestions
- Heart Failure Mortality Prediction
- Clinical Records for Heart Failure Analysis
- Cardiovascular Disease Patient Outcomes
- Predictive Analytics for Heart Failure
- Heart Failure Survival Data
Attributes
Original Data Source: Clinical Records for Heart Failure Analysis
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