SymptoSmart is a tool built for instant symptom analysis, triage, and medical recommendations. With SymptoSmart, users can input their symptoms, and the system provides instant feedback, categorizing the severity of the condition and offering tailored recommendations based on the input.
- Streamlit: A Python library that allows you to create web applications for machine learning and data science projects.
- Pickle: A module used for serializing and deserializing Python objects.
- NumPy: A fundamental package for scientific computing with Python.
- Pandas: A fast, powerful, flexible, and easy-to-use open-source data analysis and manipulation tool.
- Scikit-learn: A simple and efficient tool for predictive data analysis.
- OpenAI: An AI research laboratory consisting of the for-profit corporation OpenAI LP and its parent company, OpenAI Inc.
SymptoSmart leverages the power of Machine Learning, specifically Random Forest (RF), combined with advanced Artificial Intelligence (AI) techniques to ensure precise symptom analysis and personalized recommendations.
- Machine Learning (ML): SymptoSmart utilizes Random Forest (RF) algorithms to diagnose and triage symptoms based on user input.
- Natural Language Processing (NLP): Through NLP techniques, SymptoSmart processes and comprehends user input, enabling accurate symptom analysis.
- Vectorization: SymptoSmart employs vectorization methods to represent symptoms and diagnoses numerically, facilitating effective training and prediction for the RF model.
Contributions to SymptoSmart are welcome! If you have any suggestions, bug reports, or feature requests, please open an issue on GitHub or submit a pull request.
- This project is made possible with this dataset provided by contributers in Hugging Face.
- Special thanks to the developers and contributors of Streamlit, Pandas, Scikit-learn, and other dependencies used in this project.
- We'd also like to acknowledge the support and resources provided by OpenAI for enabling us to incorporate AI capabilities into SymptoSmart.