A Classification Tree Model for Decision-Making in Clinical Practice: An Application Based on the Data of the German Multicenter Study on Eating Disorders, Project TR-EAT

2002 ◽  
Vol 12 (4) ◽  
pp. 445-461 ◽  
Author(s):  
W. Hannöver ◽  
M. Richard ◽  
N.B. Hansen ◽  
Z. Martinovich ◽  
H. Kordy
Keyword(s):  
Decision Making ◽  
Eating Disorders ◽  
Clinical Practice ◽  
Multicenter Study ◽  
Classification Tree ◽  
Tree Model ◽  
Classification Tree Model

Collaborative evaluation and management of students’ health-related physical fitness: applications of cluster analysis and the classification tree

2012 ◽  
Vol 24 (2) ◽  
Author(s):  
Jou-An Chen ◽  
Chi-Chuan Shih ◽  
Pay-Fan Lin ◽  
Jin-Jong Chen ◽  
Kuan-Chia Lin
Keyword(s):  
Cluster Analysis ◽  
Physical Fitness ◽  
Classification Tree ◽  
Metabolic Risk ◽  
Tree Model ◽  
School Year ◽  
Classification Tree Model ◽  
Health Related ◽  
Risk Approach ◽  
Collaborative Evaluation

Abstract Health-related physical fitness has decreased with age; this is od immense concern to adolescents. School-based health intervention programs can be classified as either population-wide or high-risk approach. Although the population-wide and risk-based approaches adopt different healthcare angles, they all need to focus resources on risk evaluation. In this paper, we describe an exploratory application of cluster analysis and the tree model to collaborative evaluation of students’ health- related physical fitness from a high school sample in Taiwan (n=742). Cluster analysis show that physical fitness can be divided into relatively good, moderate and poor subgroups. There are significant differences in biochemical measurements among these three groups. For the tree model, we used 2004 school-year students as an experimental group and 2005 school-year students as a validation group. The results indicate that if sit-and-reach is shorter than 33 cm, BMI is >25.46 kg/m2, and 1600 m run/walk is >534 s, the predicted probability for the number of metabolic risk factors ≥2 is 100% and the population is 41, both results are the highest. From the risk-based healthcare viewpoint, the cluster analysis can sort out students’ physical fitness data in a short time and then narrow down the scope to recognize the subgroups. A classification tree model specifically shows the discrimination paths between the measurements of physical fitness for metabolic risk and would be helpful for self-management or proper healthcare education targeting different groups. Applying both methods to specific adolescents’ health issues could provide different angles in planning health promotion projects.

scholarly journals Screening Risk Factors and Interaction Analysis of Hypertension in Overweight and Obesity Population based on Three Statistical Models

2021 ◽  
Author(s):  
Li Lu Wei ◽  
Yu jian
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Logistic Regression ◽  
Regression Model ◽  
Bp Neural Network ◽  
Logistic Regression Model ◽  
Classification Tree ◽  
Overweight And Obesity ◽  
Tree Model ◽  
Classification Tree Model

Abstract Background Hypertension is a common chronic disease in the world, and it is also a common basic disease of cardiovascular and brain complications. Overweight and obesity are the high risk factors of hypertension. In this study, three statistical methods, classification tree model, logistic regression model and BP neural network, were used to screen the risk factors of hypertension in overweight and obese population, and the interaction of risk factors was conducted Analysis, for the early detection of hypertension, early diagnosis and treatment, reduce the risk of hypertension complications, have a certain clinical significance.Methods The classification tree model, logistic regression model and BP neural network model were used to screen the risk factors of hypertension in overweight and obese people.The specificity, sensitivity and accuracy of the three models were evaluated by receiver operating characteristic curve (ROC). Finally, the classification tree CRT model was used to screen the related risk factors of overweight and obesity hypertension, and the non conditional logistic regression multiplication model was used to quantitatively analyze the interaction.Results The Youden index of ROC curve of classification tree model, logistic regression model and BP neural network model were 39.20%,37.02% ,34.85%, the sensitivity was 61.63%, 76.59%, 82.85%, the specificity was 77.58%, 60.44%, 52.00%, and the area under curve (AUC) was 0.721, 0.734,0.733, respectively. There was no significant difference in AUC between the three models (P>0.05). Classification tree CRT model and logistic regression multiplication model suggested that the interaction between NAFLD and FPG was closely related to the prevalence of overweight and obese hypertension.Conclusion NAFLD,FPG,age,TG,UA, LDL-C were the risk factors of hypertension in overweight and obese people. The interaction between NAFLD and FPG increased the risk of hypertension.

scholarly journals A contrast-enhanced-CT-based classification tree model for classifying malignancy of solid lung tumors in a Chinese clinical population

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Xiaonan Cui ◽  
Marjolein A. Heuvelmans ◽  
Grigory Sidorenkov ◽  
Yingru Zhao ◽  
Shuxuan Fan ◽  
...  
Keyword(s):  
Classification Tree ◽  
Lung Tumors ◽  
Clinical Population ◽  
Tree Model ◽  
Classification Tree Model ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

Methodical approach to assess the condition of samples of weapons and military equipment on the basis of algorithmic trees

2021 ◽  
pp. 69-76
Author(s):  
V. Dudnyk ◽  
O. Grishchyn ◽  
V. Netrebko ◽  
R. Prus ◽  
M. Voloshcuk
Keyword(s):  
Classification Scheme ◽  
Classification Tree ◽  
Structural Complexity ◽  
Classification Trees ◽  
Training Sample ◽  
Technical Condition ◽  
Tree Model ◽  
Military Equipment ◽  
Classification Tree Model ◽  
Types Of Information

An effective mechanism for the synthesis of classification trees based on fixed initial information (in the form of a training sample) for the task of recognizing the technical condition of samples of weapons and military equipment. The constructed algorithmic classification tree (model) will unmistakably classify (recognize) the entire training sample (situational objects) according to which the classification scheme is constructed. And have a minimal structure (structural complexity) and consist of components (modules) - autonomous algorithms for classification and recognition as vertices of the structure (attributes of the tree). The developed method of building models of algorithm trees (classification schemes) allows you to work with training samples of a large amount of different types of information (discrete type). Provides high accuracy, speed and economy of hardware resources in the process of generating the final classification scheme, build classification trees (models) with a predetermined accuracy. The approach of synthesis of new algorithms of recognition (classification) on the basis of library (set) of already known algorithms (schemes) and methods is offered. Based on the proposed concept of algorithmic classification trees, a set of models was built, which provided effective classification and prediction of the technical condition of samples. The paper proposes a set of general indicators (parameters), which allows to effectively present the general characteristics of the classification tree model, it is possible to use it to select the most optimal tree of algorithms from a set based on methods of random classification trees. Practical tests have confirmed the efficiency of mathematical software and models of algorithm trees.

scholarly journals Smartphone-Based Wheel Imbalance Detection

2015 ◽  
Author(s):  
Joshua E. Siegel ◽  
Rahul Bhattacharyya ◽  
Sanjay Sarma ◽  
Ajay Deshpande
Keyword(s):  
Test Data ◽  
Classification Tree ◽  
Excitation Frequency ◽  
Principal Component ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Accelerometer Data ◽  
Tree Model ◽  
Classification Tree Model

Onboard sensors in smartphones present new opportunities for vehicular sensing. In this paper, we explore a novel application of fault detection in wheels, tires and related suspension components in vehicles. We present a technique for in-situ wheel imbalance detection using accelerometer data obtained from a smartphone mounted on the dashboard of a vehicle having balanced and imbalanced wheel conditions. The lack of observable distinguishing features in a Fourier Transform (FT) of the accelerometer data necessitates the use of supervised machine learning techniques for imbalance detection. We demonstrate that a classification tree model built using Fourier feature data achieves 79% classification accuracy on test data. We further demonstrate that a Principal Component Analysis (PCA) transformation of the Fourier features helps uncover a unique observable excitation frequency for imbalance detection. We show that a classification tree model trained on randomized PCA features achieves greater than 90% accuracy on test data. Results demonstrate that the presence or absence of wheel imbalance can be accurately detected on at least two vehicles of different make and model. Sensitivity of the technique to different road and traffic conditions is examined. Future research directions are also discussed.

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