The fact that human life is dependent on the proper functioning of the heart is the driving force behind this research. The heart is a crucial part of our bodies, and heart disease has become the leading cause of death globally. With the increase in number of deaths among people due to heart diseases, it becomes an issue to be addressed. I built an online platform to predict the risk of heart attack in a patient.
This application takes age, BMI, Systolic BP, Diastolic BP, heart rate and blood glucose levels as input to give the risk in percentage associated with having a heart attack in a patient in 10 years time.
- Check is python is installed in your system by typing python --version in your command prompt. If not then install python on your system by downloading the setup from the python website here.
- Clone the project (or download the folder to your local)
git clone https://github.com/Sjha2012/HeartAttackRiskPredictionUsingML.git - Go to project directory
cd project - Install Dependencies
pip install numpy pandas matplotlib seaborn scikit-learn boruta statsmodels imbalanced-learn
To run this project, open the code file in vs code studio and then tap on the run all option. To get the risk associated with heart attack, input the values of age, BMI, Systolic BP, Diastolic BP, heart rate and blood glucose levels and then run the corresponding cell.
Language: Python Libraries: numpy, pandas, matplotlib, scikit-learn, boruta, imbalanced-learn
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