What Challenges Exist in Implementing Supervised Learning in Epidemiology?

Despite its advantages, several challenges must be addressed:

- Data Quality: The accuracy of supervised learning models heavily relies on the quality of the input data. Incomplete or biased data can lead to inaccurate predictions.
- Interpretability: Some models, particularly deep learning algorithms, are often seen as "black boxes," making it difficult to understand how they arrive at specific predictions.
- Ethical Considerations: The use of patient data raises ethical concerns regarding privacy and consent.
- Generalization: Models trained on specific datasets may not generalize well to other populations or regions.