Several factors can influence prediction accuracy, including:
1. Data Quality: High-quality, comprehensive data is crucial for developing reliable predictive models. Poor data quality can lead to inaccurate predictions. 2. Model Complexity: More complex models may capture the nuances of cancer biology more effectively but can also be prone to overfitting. 3. Feature Selection: Choosing the right features (e.g., genetic markers, clinical history) is essential for building accurate predictive models. 4. Sample Size: Larger datasets generally provide more reliable predictions, but they must be representative of the population.
