BACKGROUND Individual signs and symptoms are of limited value for the diagnosis of influenza. OBJECTIVE To develop a decision tree for the diagnosis of influenza based on a classification and regression tree (CART) analysis. METHODS Data from two previous similar cohort studies were assembled into a single dataset. The data were randomly divided into a development set (70%) and a validation...

There are ten thousand men in the desert, suffering from intensely painful thirst. We are a group of ten thousand people near the desert, and each of us has a pint of water. We can’t go into the desert ourselves, but what we can do is pour our pints into a water cart. The cart will be driven into the desert, and any water in it will be evenly distributed amongst the men. If we pour in our pints...

The objective of this study was to develop information mining methodology for drought modeling and predictions using historical records of climate, satellite, environmental, and oceanic data. The classification and regression tree (CART) approach was used for extracting drought episodes at different time-lag prediction intervals. Using the CART approach, a number of successful model trees were ...

Purpose: Classification of gene expression helps study disease. However, it faces two obstacles: an imbalanced class and a high dimension. The motivation this is to examine the effectiveness undersampling before feature selection on high-dimensional data with classes.Methods: Least Absolute Shrinkage Selection Operator (Lasso), which can select features, handle modeling. Random (RUS) be used de...

Classification of Indian stock market data has always been a certain appeal for researchers. In this paper, first time combination of three supervised machine learning algorithms, classification and regression tree (CART) , linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) are proposed for classification of Indian stock market data, which gives simple interpretation o...

OBJECTIVES Demonstrate the application of decision trees--classification and regression trees (CARTs), and their cousins, boosted regression trees (BRTs)--to understand structure in missing data. SETTING Data taken from employees at 3 different industrial sites in Australia. PARTICIPANTS 7915 observations were included. MATERIALS AND METHODS The approach was evaluated using an occupationa...

Decision tree learning is a popular approach for classification and regression in machine learning and statistics, and Bayesian formulations— which introduce a prior distribution over decision trees, and formulate learning as posterior inference given data—have been shown to produce competitive performance. Unlike classic decision tree learning algorithms like ID3, C4.5 and CART, which work in ...

With this research, we sought to examine the performance of six different regression tree data mining methods to predict mortality in head injury. Using a data set consisting of 1603 head injury cases, we assessed the performance of: the Classification and Regression Trees (CART) method; the Chi-squared Automatic Interaction Detector (CHAID) method; the Exhaustive CHAID (E-CHAID) method; the Qu...

Integrating multiple learned classification models (classifiers) computed over large and (physically) distributed data sets has been demonstrated as an effective approach to scaling inductive learning techniques, while also boosting the accuracy of individual classifiers. These gains, however, come at the expense of an increased demand for run-time system resources. The final ensemble meta-clas...

Despite the large overall beneficial effects of endovascular treatment in patients with acute ischemic stroke, severe disability or death still occurs almost one-third patients. These patients, who might not benefit from treatment, have been previously identified traditional logistic regression models, which may oversimplify relations between characteristics and outcome, machine learning techni...