© 2022 American Society of EchocardiographyBackground: Diagnosing left ventricular diastolic dysfunction (DD) noninvasively in children is difficult as no validated pediatric diagnostic algorithm is available. The aim of this study is to explore the use of machine learning to develop a model that uses echocardiographic measurements to explain patterns in invasively measured markers of DD in children. Methods: Children at risk for developing DD were enrolled, including patients with Kawasaki disease, heart transplantation, aortic stenosis, and coarctation of the aorta when undergoing clinical left heart catheterization. Simultaneous invasive pressure measurements were made using a high-fidelity catheter (time constant of isovolumic relaxation [Tau, τ], left ventricular end-diastolic pressure, and maximum negative rate of pressure change) and echocardiographic DD measurements. Spearman correlations were performed for each echocardiographic feature with invasive markers to understand pairwise relationships. Separate random forest (RF) models were implemented to assess all echocardiographic features, key demographic data, and clinical diagnosis in predicting invasive markers. A backward stepwise regression model was simultaneously implemented as a comparative conventional reference model. The relative importance of all parameters was ranked in terms of accuracy reduction. Model approximation was then performed using a regression tree with the top-ranked features of each RF model to improve model interpretability. Regression coefficients of the linear models were presented. Results: Fifty-nine children were included. Spearman correlations were generally low. The RF models’ performance measures were noninferior to those of the linear model. However, the linear model's regression coefficients were unintuitive. The highest ranked important features for the RF models were propagation velocity for Tau, E/propagation velocity ratio for left ventricular end-diastolic pressure, and systolic global longitudinal strain rate for maximum negative rate of pressure change. Conclusions: Estimating individual components of DD can potentially improve the noninvasive assessment of pediatric DD. Although pairwise correlations measured were weak and linear regression coefficients unintuitive, approximated machine learning models aided in understanding how echocardiographic and invasive parameters of DD are related. This machine learning approach could help in further development of pediatric-specific diagnostic algorithms.