PDAC Prediction with a Urine Biomarker Penal

Conclusion

The conclusion of this study can be summarized as below:

1. Machine learning models outperform traditional statistical models in predictive capabilities, with XGBoost achieving a high accuracy of 98.3%. 

2. For model interpretation, logistic regression remains clearer, with MLE providing a better fit than Bayesian estimation. 

3. Creatine, LYVE1, and REG1B levels are the most important factors for diagnosing PDAC using urine samples, while variables such as patient cohort, sample origin, and age have some impact on the results.