Data is taken from Kaggle : https://www.kaggle.com/andrewmvd/heart-failure-clinical-data

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.

Following are the feature used for predicting heart fail Age : Age of the Person Aneamia : Decrease of red blood cells or hemoglobin
Creatinine Phosphokinase : Level of the CPK enzyme in the blood (mcg/L) Normal Range : 10 to 120 micrograms per liter (mcg/L)
Diabetes : If a person has diabetes
Ejection Fraction : Percentage of blood leaving the heart at each contraction (%)
Platelets : Platelets in the blood (kiloplatelets/mL) Normal Range : 150,000 to 450,000 platelets per microliter of blood
High Blood Pressure : If the patient has hypertension
Serum Creatinine : Level of serum creatinine in the blood (mg/dL) Normal Range : 0.84 to 1.21 milligrams per deciliter
Serum Sodium : Level of serum sodium in the blood (mEq/L) Normal Range : 135 and 145 milliequivalents per liter
Time : Follow-up period (days)

Following are the algorithms used for predicting heart fail along with their accuracy

From the above table, it is evidend that the best classifier for this problem is either XGBoost or Random Forest. So for the app building, Random Forest is considered for predicting Heart Failure.

I have used Streamlit library and Heroku platform to deploy the app.
App URL : https://heart-fail-prediction.herokuapp.com/