Heart Failure Prediction

Brief description of the data set

Cardiovascular diseases (CVDs) are the most common type of health condition causing death worldwide. Moreover, it is estimated that CVDs lead to the death of 17.9 million people around the world in 2016. This number corresponds to 31% of all global deaths(1). Because of this high death rate, understanding the reasons for CVDs is a vital issue.

Heath failure is the most common type of CVDs which is caused by the insufficient blood supply to the heart's tissue(2). Heart failure can be prevented with the help of understanding underlying causes. This data set taken from Kaggle includes 12 features that can be analyzed for clarifying the real connections between symptoms and disease.

This dataset contains 12 feature columns and 1 target column. 5 of the 12 feature columns are boolean and the rest of them are numerical. Total number of sample size is 299. The target column is a boolean type. Descriptions for the columns of the dataset can be made briefly as,

age: depict how old the person is.
anaemia: lower rate of hemoglobin (red blood cell) concentration. It depicts the patience has anemia or not.
creatinine_phosphokinase(mcg/L): it is an enzyme responsible to regulate the concentration of adenosine triphosphate (ATP) within a cell.
diabetes(boolean): high level of sugar in the blood for an extended period. It shows whether the person has diabetes or not.
ejection_fraction: a percentage measure of how much blood is left out of ventricle pumps for each contraction.
high_blood_pressure: it means blood pressure reaches an unhealthy level. This column shows the person who has hypertension or not.
platelets(kiloplatelets/mL): they are colorless cells in the blood and be in charge of stopping or preventing bleeding
serum_creatinine(mg/dL): creatinine is a waste product produced by the muscle cells. This column shows the level of serum creatinine in the blood.
serum_sodium(mEq/L): it depicts the sodium level in the blood.
sex: This column shows the gender of the patience.
smoking: it shows whether the person smokes cigarettes or not.
time: The information of how many times the patient visits the clinic in a month.
DEATH_EVENT: This is a target column show whether patience died or not.

Initial plan for data exploration