This academic project explores the use of Artificial Intelligence (AI) for diseases prediction, utilizing a dataset with over 130 symptoms and 40 diseases. The primary focus is to demonstrate the application of Machine Learning techniques in practical data analysis situations, and it does not have real medical implications.
The model, developed in the Colab environment with Python, employs the Random Forest Classifier algorithm with 100 decision trees. The system was trained and tested with a significant database, achieving an accuracy of 97.62%, demonstrating AI’s capability to interpret and analyze complex data.
Users can input symptoms through a simple interface. The model processes the data and predicts the corresponding disease. Although the project is academic in nature and not intended for medical diagnosis, it highlights the potential application of AI in healthcare and serves as an excellent educational tool to understand the fundamentals and capabilities of Machine Learning technology.