The aim of the present study was to validate, and if necessary update, a predictive model previously developed using a classification and regression tree (CART) algorithm for predicting successful extubation (ES) using a new cohort. This prospective cohort study enrolled adults admitted to 10 intensive care units, who had successfully passed a spontaneous breathing trial (SBT) and were consider...

BACKGROUND Tuberculosis (TB) remains a public health issue worldwide. The lack of specific clinical symptoms to diagnose TB makes the correct decision to admit patients to respiratory isolation a difficult task for the clinician. Isolation of patients without the disease is common and increases health costs. Decision models for the diagnosis of TB in patients attending hospitals can increase th...

decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. the resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. the most comprehensible decision trees have been designed for perfect symbolic data. classical crisp decision trees (dt) are widely applied to classification t...

Abstract Decision trees are a widely used method for classification, both alone and as the building blocks of multiple different ensemble learning methods. The Max Cut decision tree introduced here involves novel modifications to standard, baseline variant classification tree, CART Gini. One modification an alternative splitting metric, Cut, based on maximizing distance between all pairs observ...

This paper introduces a tree-based model that combines aspects of CART (Classification and Regression Trees) and STR (Smooth Transition Regression). The model is called the Smooth Transition Regression Tree (STR-Tree). The main idea relies on specifying a parametric nonlinear model through a tree-growing procedure. The resulting model can be analyzed as a smooth transition regression with multi...

Results of damage prediction in buildings can be used as a useful tool for managing and decreasing seismic risk of earthquakes. In this study, damage spectrum and C4.5 decision tree algorithm were utilized for damage prediction in steel buildings during earthquakes. In order to prepare the damage spectrum, steel buildings were modeled as a single-degree-of-freedom (SDOF) system and time-history...

Decision trees and ensembles of decision trees are very popular in machine learning and often achieve state-of-the-art performance on black-box prediction tasks. However, popular variants such as C4.5, CART, boosted trees and random forests lack a probabilistic interpretation since they usually just specify an algorithm for training a model. We take a probabilistic approach where we cast the de...

the prediction of liquefaction potential of soil due to an earthquake is an essential task in civil engineering. the decision tree is a tree structure consisting of internal and terminal nodes which process the data to ultimately yield a classification. c4.5 is a known algorithm widely used to design decision trees. in this algorithm, a pruning process is carried out to solve the problem of the...

This article uses machine learning technology to analyze the correlation of climate factors that affect crop yields, and conduct prediction comprehensive evaluation guide agricultural production. paper selects early rice crops in Guangxi as research object. Based on climatic data planting areas from 1990 2017, a cart decision tree is constructed generate random forest model between yield each g...