What Are the Challenges of Predictive Analytics in Cancer?
Despite its potential, predictive analytics in cancer faces several challenges:
Data Quality: Inconsistent, incomplete, or biased data can lead to inaccurate predictions. Complexity: Cancer is a complex disease with many variables, making it difficult to create accurate models. Interpretability: Some predictive models, especially those based on machine learning, can be "black boxes" that are difficult to interpret, hindering clinical decision-making. Ethical Concerns: Ensuring patient privacy and data security is paramount, as is addressing potential biases in predictive models.
