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.
Binary Features:
- Anaemia (Decrease of Red BLood Cells or Hemoglobin)
- High blood pressure (Hypertension)
- Diabetes (If the patient has diabetes)
- Smoking (If the patient smokes)
- Sex (Man or Women)
Continuous Features:
- Creatinine phosphokinase CPK (Level of the CPK enzyme in the blood in mcg/L)
- Ejection fraction (Percentage of blood leaving the heart at each contraction)
- Serum Sodium (Level of Sodium in the blood in mEq/L)
- Serum Creatinine (Level of Creatininie in the blood in mg/dL)
- Platelets (Platelets in the blood in kiloplatelets/mL)
- Time (Follow-up period in Days)
Target Class:
- Death Event (If the patient died during follow-up period)
Chicco, D., Jurman, G. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Med Inform Decis Mak 20, 16 (2020). https://doi.org/10.1186/s12911-020-1023-5