In this population, "high risk 2" was defined as (1) previous fasting blood glucose (FBG) levels above 5.6mmol/l or (2) classification as "high risk 1" according to the diabetes decision tree
model (Figure 1).
Similarly, 90 samples are collected again as the input for the decision tree
is a popular classifier that does not require any knowledge or parameter setting.
For mining and processing of image data phase, the study employed decision tree
. Digital image principles and decision tree
were applied to detect skin diseases using some attributes identified in a digital image of a skin.
In this study, decision tree
analysis has examined for the existence of relationships among cage, line, ASM, body weight at sexual maturity (BWSM) on egg production.
In the following example, the function of the algorithm using the decision tree
A decision tree
algorithm pertaining to the student performance analysis and prediction.
We describe early machine learning research in Ljubljana, motivated by medical diagnostic problems, in the areas of building decision trees
with Assistant, the development of Naive and Semi-Naive Bayesian classifier and its explanations of individual predictions, and the development of ReliefF and RReliefF algorithms for non-myopic evaluation of attributes in classification and regression, respectively.
In order to predict the probability that a delay incident would occur with flights from one airport to another, conventional data mining (decision tree
) and data mining using Bayesian inference were used to build prediction models.
Techniques such as reverse algorithms: decision trees
(DT), support vector machines (SVM), neural networks (NN), and genetic algorithms (GA) are among these techniques.
Conventional decision tree
rules are generally based on experience and visual interpretation of artificial settings, subject to the influence of subjective factors, and classification and regression tree (Classification And Regression Trees, CART) method can automatically select the classification characteristics and determine the node threshold value.
In terms of the attribute selection method, the decision tree
attribute selection method yields better accuracy than the other methods.