Machine Learning Techniques for the Analysis of the Influence of Blood Gasometry Parameters on Acid–Base Homeostasis in Pediatric Patients

<b>Background/Objectives:</b> The study aimed to evaluate the most significant factors that impact arterial blood gas parameters: pH, pO₂, pCO_2, and concentration of lactates. <b>Methods:</b> The study was a retrospective analysis of clinical data obtained from the patients' records hospitalized at the Department of Pediatric Anesthesiology and Intensive Care. A total of 71 patients were enrolled in the study. A total of 479 measurements were performed for arterial blood, 41 were excluded. The analysis was performed for 438 results. The artificial neural network (ANN) regression models were applied, and the Least Absolute Shrinkage and Selection Operator (LASSO) regression was used. ANNs were built considering the following activation functions: hyperbolic tangent, linear, exponential, and logistic. The following three sets were separated: training, testing, and validation. In the case of LASSO regression, the regularization was applied, excluding insignificant variables from the model. Besides the machine learning techniques, the correlation between the variables was calculated. <b>Results:</b> The correlation coefficients for regression ANN models exceeded the value for testing set of 0.92. According to the sensitivity analysis, the most significant variable for pH was cCl^-, for pO₂ it was pO₂/FiO₂, for pCO₂ it was Fshunt, and for concentration of lactates it was pH. In the case of LASSO regression for pH, the most significant factor was pCO₂, for pO₂ it was pO₂/FiO₂, for pCO₂ it was cCl^-, and for concentration of lactates it was pCO₂. <b>Conclusions:</b> The results show the usefulness of machine learning methods in analyzing complex physiological relationships. Such techniques can help improve diagnostic accuracy and optimize therapeutic management in pediatric patients.