Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worlwide. Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure.
Most cardiovascular diseases can be prevented by addressing behavioural risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity and harmful use of alcohol using population-wide strategies.
People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help.
https://www.kaggle.com/andrewmvd/heart-failure-clinical-data
Feature | Explanation | Measurement | Range |
---|---|---|---|
Age | Age of the patient | Years | [40,..., 95] |
Anaemia | Decrease of red blood cells or hemoglobin |
Boolean | 0, 1 |
High blood pressure | If a patient has hypertension | Boolean | 0, 1 |
Creatinine phosphokinase (CPK) |
Level of the CPK enzyme in the blood |
mcg/L | [23,..., 7861] |
Diabetes | If the patient has diabetes | Boolean | 0, 1 |
Ejection fraction | Percentage of blood leaving the heart at each contraction |
Percentage | [14,..., 80] |
Sex | Woman or man | Binary | 0, 1 |
Platelets | Platelets in the blood | kiloplatelets/mL | [25.01,..., 850.00] |
Serum creatinine | Level of creatinine in the blood | mg/dL | [0.50,..., 9.40] |
Serum sodium | Level of sodium in the blood | mEq/L | [114,..., 148] |
Smoking | If the patient smokes | Boolean | 0, 1 |
Time | Follow-up period | Days | [4,...,285] |
DEATH EVENT (TARGET) |
If the patient died during the follow-up period | Boolean | 0, 1 |
NOTE: mcg/L: micrograms per liter. mL: microliter. mEq/L: milliequivalents per litre
Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. (03 February 2020) https://doi.org/10.1186/s12911-020-1023-5