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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)

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