The use of Bayesian approach has the promise of features indicative of regression analysis model classification tree to take advantage of the above information by, and ensemble trees for explanatory variables are all together and at every stage on the other. In addition to obtaining the subsequent information at each node in the construction of these classification tree. Although bayesian estimates is generally accurate, but it seems that the logistic model is still a good competitor in the field of binary responses through its flexibility and mathematical representation. So is the use of three research methods data processing is carried out, namely: logistic model, and model classification regression tree, and bayesian regression tree model. Having been in this research compare these methods form a model for additive function to some nonparametric function. It was a trade-off between these process models based on the classification accuracy by misclassification error, and estimation accuracy by the root of the mean squares error: RMSE. It was the application on patients with diabetes data for those aged 15 years and below are taken from the sample size (200) was withdrawn from the Children Hospital in Al-Eskan / Baghdad.
Bayesian Method in Classification Regression Tree to estimate nonparametric additive model compared with Logistic Model with Application
Regression Tree
Artificial Intelligence
Additive Model.
Publication Date
Thu Jan 01 2026
Journal Name
Innovations In Sustainable Sports Infrastructure For Eco-friendly Design And Operations
Redacting The Performance of 110m Hurdle Runners Through the Analysis of Physical and Functional Abilities
Hurdling
Sprinting
Performance
Speed
Strength Further studies could increase sample size and employ machine learning to improve the model by including more biomechanical and psychological predictors
increasing the accuracy and applicability of performance predictions in sprint hurdling.
The performance in the 110-meter hurdles at the sprint hurdles event is determined by several physical and physiological qualities. Nonetheless, relatively little attention has been paid to the predictability of such factors in determining race performance. This study seeks to fill this gap by establishing the most critical physical and physiological characteristics affecting elite hurdlers’ performance and creating a statistical model that predicts race times from the identified measurable characteristics. The study utilized a descriptive research design in-volving six elite male hurdlers, all of whom completed a battery of standardized physical and functional tests to assess their explosive lower-body strength, agility, reaction
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