External validation typically involves several steps:
Dataset Selection: Choosing a different dataset that represents a population or condition similar to the original study. Model Application: Applying the developed model to the new dataset. Performance Assessment: Measuring the model's performance using metrics such as sensitivity, specificity, and area under the curve (AUC). Comparative Analysis: Comparing the performance metrics of the original and new datasets to assess consistency.
