Early Heart Attack Prediction using Flask

This project is an implementation of a machine learning model that predicts the likelihood of an early heart attack based on a set of input parameters. The model has been trained on a dataset containing various risk factors, and it achieves an accuracy of 91.8%.

Team Members

• Sonish Maharjan
• Gaurav Giri
• Pratibha Kulung

Prerequisites

To run this project, you will need:

• Python 3.6 or higher
• Flask
• NumPy
• Pandas
• Scikit-learn
• XGBoost

You can install these dependencies by running:

pip install -r requirements.txt

Usage

To run the model, execute the following command:

python3 -m flask run

This will start the Flask server, and you can access the prediction form by visiting http://localhost:5000/ in your web browser.

Models Used

The following models have been used for training the dataset:

• K-Nearest Neighbors (KNN) Classifier
• Logistic Regression
• Decision Tree Classifier
• Random Forest Classifier
• Support Vector Machine (SVM) Classifier
• Gaussian Naive Bayes Classifier
• XGBoost Classifier
• Voting Classifier (Combination of all classifiers)