Machine learning for real-time prediction of complications induced by flexible uretero-renoscopy with laser lithotripsy

It is not always easy to predict the outcome of a surgery. Peculiarly, when talking about the risks associated to a given intervention or the possible complications that it may bring about. Thus, predicting those potential complications that may arise during or after a surgery will help minimize risks and prevent failures to the greatest extent possible. Therefore, the objectif of this article is to propose an intelligent system based on machine learning, allowing predicting the complications related to a flexible uretero-renoscopy with laser lithotripsy for the treatment of kidney stones. The proposed method achieved accuracy with 100% for training and, 94.33% for testing in hard voting, 100% for testing and 95.38% for training in soft voting, with only ten optimal features. Additionally, we were able to evaluted the machine learning model by examining the most significant features using the shpley additive explanations (SHAP) feature importance plot, dependency plot, summary plot, and partial dependency plots

Minimaxity and Admissibility of Predictive Density Estimators Under S-Hellinger Distances

In this paper, we consider the study of the efficiency of predictive density estimators of multivariate observables measured by the frequentist risk corresponding to S-Hellinger distances as a set of loss functions (for every α ∈ [0, 1]). The main themes, revolve around the inefficiency of minimum risk equivariant (MRE) predictors in high enough dimensions and about the inefficiency of plug-in estimators. We improve the plug-in for a dual point estimation loss with or without expanding the scale. A link between the S-Hellinger distances risk of plug-in type estimators and the risk under reflected normal loss for point estimation is established, bringing into play all the established literature on Stein type dominators. Further, we suggest dominant estimators with or without the presence of restrictions on the unknown mean parameter. Ultimately we prove under the new measure of goodness-of-fit dominance results under a restricted parameter space (multivariate and univariate).Key words: S-Hellinger Distances, Minimaxity, Admissibility, Stein estimation, concave loss, Predictive density estimatio