Understanding the interplay between dataset bias and imbalance in the context of PTSD and depression diagnosis can enhance the models' performance:
Balanced Dataset: Collecting a more balanced dataset with equal representation of individuals with PTSD and depression can help mitigate bias and improve model generalization.
Feature Importance Analysis: Conducting a thorough feature importance analysis to identify the most influential features in the diagnosis process, considering the unique characteristics of PTSD and depression comorbidity.
Rebalancing Techniques: Implementing techniques like oversampling or undersampling to balance the dataset and reduce the impact of dataset bias on model performance.
Cross-Validation: Utilizing cross-validation techniques to assess model performance across different subsets of the dataset and ensure robustness in handling dataset bias and imbalance.