Fig. 1: Comparison of frequentist and Bayesian (Bayes-splenic hilum lymph node metastasis [SHLNM]) models in predicting metastasis probability for SHLN dissection in upper gastrointestinal cancer.
From: Establishment of a machine learning model for predicting splenic hilar lymph node metastasis
This figure illustrates the different approaches of frequentist and Bayesian logistic regression models in predicting the probability of SHLN metastasis in patients who underwent total gastrectomy with splenectomy. The frequentist model (top pathway) uses clinical, tumor, lymph node location, and pathological information to generate a single-point probability estimate (e.g., 72%) for metastasis. In contrast, the Bayesian model (bottom pathway) incorporates the same data sources but outputs a posterior probability distribution (PPD), offering a more comprehensive view of the uncertainty associated with the prediction. The PPD allows for visualization of the range of possible outcomes and highlights the degree of uncertainty, which is critical for informed clinical decision-making in scenarios with high risk and uncertainty.
