Using data mining to predict emergency department length of stay greater than 4 hours: Derivation and single‐site validation of a decision tree algorithm

2019 ◽  
Vol 32 (3) ◽  
pp. 416-421 ◽  
Author(s):  
Md Anisur Rahman ◽  
Bridget Honan ◽  
Thomas Glanville ◽  
Peter Hough ◽  
Katie Walker
Keyword(s):  
Data Mining ◽  
Emergency Department ◽  
Length Of Stay ◽  
Decision Tree ◽  
Single Site ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Using Data

scholarly journals SENTIMENT ANALYSIS ON TWITTER OF PSBB EFFECT USING MACHINE LEARNING

2020 ◽  
Vol 17 (2) ◽  
pp. 143-150
Author(s):  
Irwansyah Saputra ◽  
Jose Andrean Halomoan ◽  
Adam Bagusmugi Raharjo ◽  
Cyra Rezky Ananda Syavira
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Tree ◽  
Sentiment Analysis ◽  
Bayes Classifier ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Processing Data ◽  
Twitter Users ◽  
Using Data

A collection of tweets from Twitter users about PSBB can be used as sentiment analysis. The data obtained is processed using data mining techniques (data mining), in which there is a process of mining the text, tokenize, transformation, classification, stem, etc. Then calculated into three different algorithms to be compared, the algorithm used is the Decision Tree, K-NN, and Naïve Bayes Classifier to find the best accuracy. Rapidminer application is also used to facilitate writers in processing data. The highest results from this study were the Decision Tree algorithm with an accuracy of 83.3%, precision 79%, and recall 87.17%.

An Analysis of Factors Influencing Foreign Language Self-Efficacy Based on C5.0 Decision Tree Algorithm in Data Mining

2014 ◽  
Vol 538 ◽  
pp. 460-464
Author(s):  
Xue Li
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Foreign Language ◽  
Language Learning ◽  
Learning Strategies ◽  
Self Efficacy ◽  
Foreign Language Learning ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C5.0 Decision Tree

Based on inter-correlation and permeability among disciplines, the author makes an attempt to apply the information science to cognitive linguistics to provide a new perspective for the study of foreign languages. The correlation between self-efficacy and such four factors as anxiety, learning strategies, motivation and learners’ past achievement is analyzed by means of data mining and the extent to which the above factors affect self-efficacy in language learning is explored in this paper. The paper employs the decision tree algorithm in SPSS Clementine. C5.0 decision tree algorithm is adopted to analyze data in the study. The results are elicited from the researches carried out in this paper. The increased anxiety is bound to weaken learners’ motivation over time. It is obvious that learners have low self-efficacy. It is very important to employ strategies in foreign language learning. Ignorance of using learning strategies may result in unplanned learning with unsatisfactory achievements in spite of more efforts involved. Self-efficacy in foreign language learning may be weakened accordingly. Learners’ past achievement is a reference dimension in measuring self-efficacy with weaker influence.

scholarly journals CUDT: A CUDA Based Decision Tree Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Win-Tsung Lo ◽  
Yue-Shan Chang ◽  
Ruey-Kai Sheu ◽  
Chun-Chieh Chiu ◽  
Shyan-Ming Yuan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
New Technology ◽  
Large Data ◽  
Decision Tree Algorithm ◽  
Data Set ◽  
Tree Algorithm ◽  
Ubiquitous Sensing ◽  
Device Architecture ◽  
Huge Data

Decision tree is one of the famous classification methods in data mining. Many researches have been proposed, which were focusing on improving the performance of decision tree. However, those algorithms are developed and run on traditional distributed systems. Obviously the latency could not be improved while processing huge data generated by ubiquitous sensing node in the era without new technology help. In order to improve data processing latency in huge data mining, in this paper, we design and implement a new parallelized decision tree algorithm on a CUDA (compute unified device architecture), which is a GPGPU solution provided by NVIDIA. In the proposed system, CPU is responsible for flow control while the GPU is responsible for computation. We have conducted many experiments to evaluate system performance of CUDT and made a comparison with traditional CPU version. The results show that CUDT is 5∼55 times faster than Weka-j48 and is 18 times speedup than SPRINT for large data set.

The Analysis and Application of the C4.5 Algorithm in Decision Tree Technology

2012 ◽  
Vol 457-458 ◽  
pp. 754-757
Author(s):  
Hong Yan Zhao
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Teaching Quality ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree ◽  
Result Analysis

The Decision Tree technology, which is the main technology of the Data Mining classification and forecast, is the classifying rule that infers the Decision Tree manifestation through group of out-of-orders, the non-rule examples. Based on the research background of The Decision Tree’s concept, the C4.5 Algorithm and the construction of The Decision Tree, the using of C4.5 Decision Tree Algorithm was applied to result analysis of students’ score for the purpose of improving the teaching quality.

The Method of Attribute Reduction Based on Decision Tree

2011 ◽  
Vol 230-232 ◽  
pp. 1303-1307 ◽  
Author(s):  
Fa Chao Li ◽  
Hong Ze Yin ◽  
Fei Guan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Reduction Method ◽  
Attribute Reduction ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
The Core ◽  
Basic Ideas ◽  
Specific Implementation ◽  
Implementation Steps

This paper is for refining database in the data mining process. Based on the analysis of the features and disadvantages of this decision tree algorithm and the substantive characteristics of data mining, we propose the concept of the core samples set and prove its invariance. On this basis, we build an attribute reduction method based on decision tree algorithm and then give a specific implementation steps, further, combined with a specific instance analyze the characteristics and efficiency of the method. Results show that the attribute reduction method based on the decision tree has good maneuverability and explicableness. This method can simply realize the attribute reduction of information system and its basic ideas completely adapt to the attribute reduction problems of the uncertain environment.

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