It's about Kaggle dataset
Images - it's a folder where are all pictures which I used in Jupiter
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.
age
anaemia Decrease of red blood cells or hemoglobin (boolean)
creatinine_phosphokinase Level of the CPK enzyme in the blood (mcg/L)
diabetes If the patient has diabetes (boolean)
ejection_fraction Percentage of blood leaving the heart at each contraction (percentage)
high_blood_pressure If the patient has hypertension (boolean)
platelets Platelets in the blood (kiloplatelets/mL)
serum_creatinine Level of serum creatinine in the blood (mg/dL)
serum_sodium Level of serum sodium in the blood (mEq/L)
sex Woman or man (binary)
smoking If the patient smokes or not (boolean)
time Follow-up period (days)
DEATH_EVENT If the patient deceased during the follow-up period (boolean)
Davide Chicco, Giuseppe Jurman: Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making 20, 16 (2020). (link)