scholarly journals Analysis on Influencing Factors of Dance Teaching Effect in Colleges Based on Data Analysis and Decision Tree Model

2020 ◽  
Vol 15 (09) ◽  
pp. 245 ◽  
Author(s):  
Beibei Guo
Keyword(s):  
Data Analysis ◽  
Decision Tree ◽  
Influencing Factors ◽  
Decision Tree Model ◽  
Analysis Model ◽  
Tree Model ◽  
Factors Affecting ◽  
New Era ◽  
Research Findings ◽  
Set Up

In colleges, dance teaching is influenced by a variety of factors. It is very difficult to clarify how much each factor impacts the teaching effect. To overcome the difficulty, this paper explores the factors affecting the dance teaching effect in colleges based on data analysis and decision tree model. Firstly, the authors enumerated the goals of dance teaching for college students in the new era, and then summed up the constraints on the influencing factors of dance teaching effect in colleges. On this basis, an analysis model was established for the influencing factors, while the corresponding extensible decision tree was set up and verified through example analysis. The research findings shed new light on the theories of dance teaching in colleges, and provide an analysis model with great application potential.

Chatter identification in thin-wall milling using an adaptive variational mode decomposition method combined with the decision tree model

2020 ◽  
pp. 095440542093370 ◽  
Author(s):  
Ruoqi Wang ◽  
Jinbo Niu ◽  
Yuwen Sun
Keyword(s):  
Decision Tree ◽  
Milling Process ◽  
Decision Tree Model ◽  
Thin Wall ◽  
Variational Mode Decomposition ◽  
Tree Model ◽  
Chatter Identification ◽  
Mode Decomposition ◽  
Set Up ◽  
Main Components

Chatter is prone to occur in thin-wall part milling process due to the low stiffness and damping of the workpiece. It roughens the machining surface, shortens the tool life, and thus should be detected and prevented. However, the multimode and time-varying dynamics of thin-wall parts produces nonstationary and multicomponent cutting signals, which makes it challenging to accurately identify the chatter occurrence. In this article, an effective chatter identification method based on adaptive variational mode decomposition and decision tree is presented to tackle this problem. The adaptive variational mode decomposition is used to adaptively decompose the raw acoustic signal into several subsignals, and the decision tree is used to automatically determine the chatter threshold. First, the convergence of multicenter-frequency signal processing is analyzed and is proved to be closely related to the accuracy of variational mode decomposition. Afterward, a criterion is set up to initialize the center frequencies of variational mode decomposition, on the basis of which an adaptive energy ratio-based method with good computational efficiency is presented to extract the frequencies of the main components from the raw signal. The initial center frequencies and the number of modes of variational mode decomposition are simultaneously obtained. As a result, the raw signal is adaptively decomposed into several subsignals, which contain its principal components. Then, the normalized energy and sample entropy of the subsignals are selected to establish the decision tree model for automatic chatter identification. Several milling tests on a thin-wall plate are carried out to verify the proposed method. The results show that chatter can be identified accurately and efficiently using the proposed method.

A novel enhanced decision tree model for detecting chronic kidney disease

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Avijit Kumar Chaudhuri ◽  
Deepankar Sinha ◽  
Dilip K. Banerjee ◽  
Anirban Das
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Decision Tree ◽  
Decision Tree Model ◽  
Tree Model

scholarly journals Risk of Pre-Malignancy or Malignancy in Postmenopausal Endometrial Polyps: A CHAID Decision Tree Analysis

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1094
Author(s):  
Michael Wong ◽  
Nikolaos Thanatsis ◽  
Federica Nardelli ◽  
Tejal Amin ◽  
Davor Jurkovic
Keyword(s):  
Decision Tree ◽  
Expectant Management ◽  
Decision Tree Model ◽  
Decision Tree Analysis ◽  
Focal Lesions ◽  
Tree Model ◽  
Normal Endometrium ◽  
Endometrial Polyps ◽  
Tree Analysis ◽  
Interaction Detection

Background and aims: Postmenopausal endometrial polyps are commonly managed by surgical resection; however, expectant management may be considered for some women due to the presence of medical co-morbidities, failed hysteroscopies or patient’s preference. This study aimed to identify patient characteristics and ultrasound morphological features of polyps that could aid in the prediction of underlying pre-malignancy or malignancy in postmenopausal polyps. Methods: Women with consecutive postmenopausal polyps diagnosed on ultrasound and removed surgically were recruited between October 2015 to October 2018 prospectively. Polyps were defined on ultrasound as focal lesions with a regular outline, surrounded by normal endometrium. On Doppler examination, there was either a single feeder vessel or no detectable vascularity. Polyps were classified histologically as benign (including hyperplasia without atypia), pre-malignant (atypical hyperplasia), or malignant. A Chi-squared automatic interaction detection (CHAID) decision tree analysis was performed with a range of demographic, clinical, and ultrasound variables as independent, and the presence of pre-malignancy or malignancy in polyps as dependent variables. A 10-fold cross-validation method was used to estimate the model’s misclassification risk. Results: There were 240 women included, 181 of whom presented with postmenopausal bleeding. Their median age was 60 (range of 45–94); 18/240 (7.5%) women were diagnosed with pre-malignant or malignant polyps. In our decision tree model, the polyp mean diameter (≤13 mm or >13 mm) on ultrasound was the most important predictor of pre-malignancy or malignancy. If the tree was allowed to grow, the patient’s body mass index (BMI) and cystic/solid appearance of the polyp classified women further into low-risk (≤5%), intermediate-risk (>5%–≤20%), or high-risk (>20%) groups. Conclusions: Our decision tree model may serve as a guide to counsel women on the benefits and risks of surgery for postmenopausal endometrial polyps. It may also assist clinicians in prioritizing women for surgery according to their risk of malignancy.

scholarly journals Reanalysis and External Validation of a Decision Tree Model for Detecting Unrecognized Diabetes in Rural Chinese Individuals

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Zhong Xin ◽  
Lin Hua ◽  
Xu-Hong Wang ◽  
Dong Zhao ◽  
Cai-Guo Yu ◽  
...  
Keyword(s):  
Decision Tree ◽  
Predictive Value ◽  
Early Stage ◽  
Current Model ◽  
External Validation ◽  
Area Under The Curve ◽  
Decision Tree Model ◽  
Tree Model ◽  
Chinese Adult ◽  
Significant Difference

We reanalyzed previous data to develop a more simplified decision tree model as a screening tool for unrecognized diabetes, using basic information in Beijing community health records. Then, the model was validated in another rural town. Only three non-laboratory-based risk factors (age, BMI, and presence of hypertension) with fewer branches were used in the new model. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) for detecting diabetes were calculated. The AUC values in internal and external validation groups were 0.708 and 0.629, respectively. Subjects with high risk of diabetes had significantly higher HOMA-IR, but no significant difference in HOMA-B was observed. This simple tool will help general practitioners and residents assess the risk of diabetes quickly and easily. This study also validates the strong associations of insulin resistance and early stage of diabetes, suggesting that more attention should be paid to the current model in rural Chinese adult populations.

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