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Heart-Failure-Prediction

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)

References

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