The Heart Attack Prediction Project is designed to assess the risk of heart attacks based on various medical attributes using a machine learning model. This project integrates a predictive model into a user-friendly website to help users evaluate their heart health risk.

Predictive Analysis: Utilizes a Logistic regression model to predict heart attack risk based on user input. Interactive Chatbot: Provides guidance and information on heart health via an advanced chatbot integrated using OpenAI's API. Secure User Authentication: Ensures that access to the chatbot and prediction features is secure and user-specific. Responsive Web Design: Crafted using Hono for backend, Bootstrap for responsive design, and Alpine.js for frontend dynamics.

Hono: Lightweight web framework used for backend operations. Bootstrap & Alpine.js: Used for crafting a responsive and dynamic frontend. OpenAI: Powers the intelligent chatbot. Heroku: Hosts the SVM model. MySQL: Manages user data and authentication securely.

├── server/               # Server-side scripts and configuration
├── client/               # Client-side HTML, CSS, and JavaScript files
├── model/                # Machine learning model files and scripts
├── data/                 # Dataset and data processing scripts
├── README.md             # Project documentation
└── .gitignore            # Specifies intentionally untracked files to ignore

Clone the Repository git clone https://github.com/yourusername/heart-attack-prediction.git cd heart-attack-prediction

bash npm install

npm run dev

Open your browser and visit http://localhost:3000 to view the application.

Login/Signup: Create an account and log in. Enter Medical Details: Input your medical details like age, cholesterol levels, etc. Get Prediction: Submit your details to receive a heart attack risk assessment. Chat with Dana: Create an assistant on openai and then call the Assistant ID in the backend.

Contributions are welcome! Please feel free to submit a pull request or open an issue to discuss potential changes or additions.

This project is licensed under the MIT License - see the LICENSE file for details.

Project Supervisor: Prof. Ahmed Banafa Partner: Adebodun Adeleye, https://github.com/Debodun Consultants: Dr. Chollette Olisah (Machine Learning Model), https://github.com/chollette Benqoder (Fullstack Developer), https://github.com/benqoder Kenechukwu Aniekwena (Product Development), https://github.com/fessor10