scholarly journals Intelligent Medical Auxiliary Diagnosis Algorithm Based on Improved Decision Tree

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Yuntao Wei ◽  
Xiaojuan Wang ◽  
Meishan Li
Keyword(s):  
Decision Tree ◽  
Classification Performance ◽  
Optimal Decision ◽  
Analysis Method ◽  
Analysis Model ◽  
Tree Model ◽  
Pathological Characteristics ◽  
Adaptive Classification ◽  
Dependent Variables ◽  
Physiological Indexes

In order to address the problem of low ability of intelligent medical auxiliary diagnosis (IMAD), an IMAD based on improved decision tree is proposed. Firstly, the constraint parameter model of IMAD is constructed. Secondly, according to the physiological indexes of IMAD, the independent variables and dependent variables of auxiliary diagnosis are constructed, the quantitative recurrent analysis of IMAD is carried out by using regression analysis method, the data analysis model of IMAD is constructed, and the adaptive classification and recognition of IMAD are carried out. Finally, the attribute feature quantity of IMAD with pathological characteristics is extracted, and the improved decision tree model is used to realize intelligent medical auxiliary, assist in the optimal decision of diagnosis, and realize the effective classification and recognition of pathological characteristics. The results show that this method has better decision-making ability and better classification performance for IMAD, which improves the intelligence and accuracy of intelligent medical auxiliary diagnosis.

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.

scholarly journals HYPER HEURISTIC EVOLUTIONARY APPROACH FOR CONSTRUCTING DECISION TREE CLASSIFIERS

2021 ◽  
Vol 20 (Number 2) ◽  
pp. 249-276
Author(s):  
Sunil Kumar ◽  
Saroj Ratnoo ◽  
Jyoti Vashishtha
Keyword(s):  
Decision Tree ◽  
Heuristic Approach ◽  
Decision Tree Model ◽  
Evolutionary Approach ◽  
Optimal Decision ◽  
Decision Tree Classifier ◽  
Tree Model ◽  
Tree Construction ◽  
Tree Classifier ◽  
Optimal Values

Decision tree models have earned a special status in predictive modeling since these are considered comprehensible for human analysis and insight. Classification and Regression Tree (CART) algorithm is one of the renowned decision tree induction algorithms to address the classification as well as regression problems. Finding optimal values for the hyper parameters of a decision tree construction algorithm is a challenging issue. While making an effective decision tree classifier with high accuracy and comprehensibility, we need to address the question of setting optimal values for its hyper parameters like the maximum size of the tree, the minimum number of instances required in a node for inducing a split, node splitting criterion and the amount of pruning. The hyper parameter setting influences the performance of the decision tree model. As researchers, we know that no single setting of hyper parameters works equally well for different datasets. A particular setting that gives an optimal decision tree for one dataset may produce a sub-optimal decision tree model for another dataset. In this paper, we present a hyper heuristic approach for tuning the hyper parameters of Recursive and Partition Trees (rpart), which is a typical implementation of CART in statistical and data analytics package R. We employ an evolutionary algorithm as hyper heuristic for tuning the hyper parameters of the decision tree classifier. The approach is named as Hyper heuristic Evolutionary Approach with Recursive and Partition Trees (HEARpart). The proposed approach is validated on 30 datasets. It is statistically proved that HEARpart performs significantly better than WEKA’s J48 algorithm in terms of error rate, F-measure, and tree size. Further, the suggested hyper heuristic algorithm constructs significantly comprehensible models as compared to WEKA’s J48, CART and other similar decision tree construction strategies. The results show that the accuracy achieved by the hyper heuristic approach is slightly less as compared to the other comparative approaches.

Management of Guidance and Counseling on Learning Discipline

2020 ◽  
Vol 10 (2) ◽  
pp. 60-74
Author(s):  
Muhammad Syauqi Mubarok
Keyword(s):  
Statistical Analysis ◽  
Descriptive Analysis ◽  
Field Studies ◽  
Guidance And Counseling ◽  
Analysis Method ◽  
Vocational High School ◽  
Analysis Model ◽  
Survey Techniques ◽  
Analysis Technique ◽  
Path Analysis Model

This article aims to examine and describe the influence of guidance and counseling management on learning discipline. The method used in this research is descriptive analysis method using survey techniques. Data collection techniques that used are documentation studies and field studies. Moreover, the data analysis technique that has been used to answer the research hypothesis is statistical analysis with a path analysis model. The location of the study was at the Ciledug Vocational High School Al-Musaddadiyah Garut, with 85 respondents taking part in the survey. The results of the discussion show that guidance and counseling management has a positive and significant effect on the discipline of learning

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 Decision Tree-Based Adaptive Reconfigurable Cache Scheme

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 176
Author(s):  
Wei Zhu ◽  
Xiaoyang Zeng
Keyword(s):  
Decision Tree ◽  
Adaptive Algorithms ◽  
Memory Access ◽  
Access Time ◽  
Decision Tree Algorithm ◽  
Verilog Hdl ◽  
Tree Model ◽  
Cache Associativity ◽  
Cache Scheme ◽  
Reconfigurable Cache

Applications have different preferences for caches, sometimes even within the different running phases. Caches with fixed parameters may compromise the performance of a system. To solve this problem, we propose a real-time adaptive reconfigurable cache based on the decision tree algorithm, which can optimize the average memory access time of cache without modifying the cache coherent protocol. By monitoring the application running state, the cache associativity is periodically tuned to the optimal cache associativity, which is determined by the decision tree model. This paper implements the proposed decision tree-based adaptive reconfigurable cache in the GEM5 simulator and designs the key modules using Verilog HDL. The simulation results show that the proposed decision tree-based adaptive reconfigurable cache reduces the average memory access time compared with other adaptive algorithms.

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.

Study on the Effect Factors of Non-Perormance Loan Ratio of Chinese Commerical Banks

2011 ◽  
Vol 50-51 ◽  
pp. 728-732
Author(s):  
Ping Li ◽  
Ming Ying Zhuo ◽  
Li Chao Feng ◽  
Rui Zhang
Keyword(s):  
Principal Component Analysis ◽  
Commercial Banks ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Method ◽  
Analysis Model ◽  
Principal Component Analysis Model ◽  
Effect Factors ◽  
Financial Indicator ◽  
Commerical Banks

Non-performance loan ratio is one of the important assessment criteria of the security of credit assets. It is also an important financial indicator to evaluate the general strength of commercial banks. Using principal component analysis method and statistical software SPSS16.0 and based on the non-performance loan ratio and relative data of some commercial banks in China in 2007, this paper provided a principal component analysis model for the non-performance loan ratio of China’s commercial banks. The factors that affect the non-performance loan ratio were refined in this paper. Finally, the characteristics of effect factors of each bank were analyzed and compared in detail.

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