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Frontend for Medical Prediction Models

Welcome to the frontend repository for our medical prediction models. This project provides a user-friendly interface for three machine learning models: stroke detection, heart attack prediction, and Titanic survival prediction. The models are designed to assist in early detection and prediction, potentially saving lives through timely interventions.

Table of Contents

• Overview
• Features
• Installation
• Usage
• Model Details
• Contributing
• License

Overview

This frontend application provides a simple and intuitive interface for interacting with three different machine learning models:

1. Stroke Detection: Predicts the likelihood of a stroke based on user input data.
2. Heart Attack Prediction: Estimates the risk of a heart attack using relevant health metrics.
3. Titanic Survival Prediction: Predicts the chances of survival on the Titanic given certain passenger details.

Features

• User-Friendly Interface: Easy-to-navigate forms to input data for each prediction model.
• Real-time Predictions: Instantly get predictions after submitting the input data.
• Detailed Results: View comprehensive prediction results and relevant risk factors.

Installation

Prerequisites

• Node.js (v14.0.0 or higher)
• npm (v6.0.0 or higher)

Steps

1. Clone the Repository:

   git clone https://github.com/yourusername/medical-prediction-frontend.git
   cd medical-prediction-frontend

2. Install Dependencies:

   npm install

3. Start the Development Server:

   npm start

The application should now be running on http://localhost:3000.

Usage

1. Stroke Detection:

   • Navigate to the Stroke Detection section.
   • Fill in the required health data such as age, hypertension, heart disease, etc.
   • Click on the 'Predict' button to get the prediction.

2. Heart Attack Prediction:

   • Go to the Heart Attack Prediction section.
   • Input the necessary health metrics like cholesterol levels, resting blood pressure, etc.
   • Submit the form to receive the prediction.

3. Titanic Survival Prediction:

   • Access the Titanic Prediction page.
   • Enter passenger details like age, gender, class, etc.
   • Click on 'Predict' to see the survival prediction.

Model Details

Stroke Detection Model

• Input Features: Age, Hypertension, Heart Disease, Ever Married, Work Type, Residence Type, Average Glucose Level, BMI, Smoking Status.
• Algorithm: Logistic Regression / Random Forest / Other (Specify the algorithm used).

Heart Attack Prediction Model

• Input Features: Age, Sex, Chest Pain Type, Resting Blood Pressure, Cholesterol, Fasting Blood Sugar, Resting ECG, Maximum Heart Rate Achieved, Exercise Induced Angina, ST Depression.
• Algorithm: Support Vector Machine / Decision Tree / Other (Specify the algorithm used).

Titanic Survival Prediction Model

• Input Features: Age, Gender, Passenger Class, Siblings/Spouses Aboard, Parents/Children Aboard, Fare.
• Algorithm: Decision Tree / K-Nearest Neighbors / Other (Specify the algorithm used).

Contributing

We welcome contributions to improve the project. To contribute, follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes and commit them (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature-branch).
5. Create a new Pull Request.

Please ensure your code follows our coding conventions and is well-documented.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

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Feel free to open issues or contact us if you have any questions or need further assistance. Happy coding!