Fig 9. Feature selection process to reduce variables for predicting acute peritonitis.

(A) Multicollinearity was addressed before generating linear models with redundant features removed prior to further analysis. (B) Principal component analysis shows that patients with acute peritonitis are discernible from stable controls. (C) L1 restricted modelling with a linear support vector machine reveals that neutrophils are the most predictive feature. (D) A simple cutoff applied to neutrophils is predictive of acute peritonitis in this cohort and is demonstrated by a shallow decision tree, where gini index is the chosen criterion for measuring the quality of split.