Welcome to an exciting journey of building a disease predicting web application! Whether you’re an aspiring data scientist or someone passionate about healthcare technology, this guide will walk you through the steps needed to create your own web app that predicts diseases like Malaria, Pneumonia, Diabetes, and more.
What is This Project About?
This project, one of the major undertakings in my undergraduate program, leverages the capabilities of machine learning to provide predictive insights into various health conditions. Using specific models trained on various datasets, the web app is designed to assist users by predicting potential diseases based on input data.
Joining Our Community
To facilitate discussions and foster collaboration on this project, I’ve created a Telegram channel where you can share questions and improvements. Feel free to join our Telegram channel and become part of our vibrant community!
How to Use This Project
To start using the disease predicting web app, follow these steps:
- Clone the project repository where the code and models are stored.
- Navigate to the directory containing the project.
- In your terminal, set the Flask application environment variable and run the app with the following commands:
$ set FLASK_APP= app.py
$ flask runThe web app is deployed on Heroku Cloud, making it accessible to everyone. You can access it live at: myml-mtapp.herokuapp.com.
Models Used
The web app utilizes several machine learning models for disease prediction, detailed below:
- Cancer model: modelb
- Diabetes model: model1b
- Heart disease model: model2b
- Liver disease model: model4b
- Kidney disease model: model3b
- Malaria model: model111.h5
- Pneumonia model: my_model.h5
Datasets for Model Development
Each model has been trained using specific datasets:
- Cancer: cancer.csv (In the repository)
- Diabetes: diabetes.csv (In the repository)
- Heart: heart.csv (In the repository)
- Liver: Indian Liver Patient Records
- Kidney: Kidney Disease Dataset
- Malaria: Cell Images for Detecting Malaria
- Pneumonia: Chest X-ray Pneumonia Dataset
Tools Used for Development
The following tools and libraries were utilized in the project:
- Python (version 3.7)
- Flask
- OpenCV
- Pandas
- Numpy
- HTML
- CSS
Troubleshooting Tips
If you encounter any issues while running the app, consider the following troubleshooting steps:
- Ensure you have all the required dependencies installed in your Python environment.
- Check if the Flask server starts without errors.
- Make sure you are using the correct model files.
- If you need assistance, don’t hesitate to reach out in our Telegram channel.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
Thank you for your interest in our disease predicting web app! Together, let’s contribute to better health outcomes using the power of technology.
