Academic Analytics: Predicting Success in the Licensure Examination of Graduates using CART Decision Tree Algorithm

Clarin, Jeffrey A.

doi:10.5373/jardcs/v12sp1/20201057

Cited by 8 publications

(7 citation statements)

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“…ID3, C4.5, and CART all adopt greedy method 0, and the top-down recursive method is just suitable for the construction method of decision tree, so decision tree adopts this method [ 19 , 20 ]. The process of the decision tree algorithm is shown in Figure 4 .…”

Section: Decision Tree Algorithmmentioning

confidence: 99%

Identification Method of Influencing Factors of Hospital Catering Service Satisfaction Based on Decision Tree Algorithm

Liu

et al. 2022

Applied Bionics and Biomechanics

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Entering the 21st century, material abundance has been greatly enriched, and living standards have been continuously improved. Now society is gradually moving towards the era of experience economy. From the perspective of experience economy, patients’ demands for hospitals are not only the satisfaction of medical technology, but their catering consumption also has begun to change to the pursuit of higher requirements. Decision tree algorithm is a kind of data mining algorithm. Data mining technology is a young technology for data analysis. It can simulate mathematical models or algorithms through data analysis, which greatly improves the prediction accuracy. This paper aims to study how to identify the influencing factors of hospital catering service satisfaction, and proposes the application of decision tree algorithm to the hospital catering service satisfaction research, and proposes decision tree-related algorithms, such as ID3, C4.5, and C5.0. Based on the analysis of patients’ satisfaction with the hospital catering service in a certain hospital, the results of the model study based on the decision tree algorithm show that the risk estimation value of the training set is 0.064, and the total correct percentage is 93.6%. The risk estimate for the test set was 0.065, for a total correct percentage of 93.5%. It can be seen that the effect of the model is good and can be effectively predicted.

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Section: Decision Tree Algorithmmentioning

confidence: 99%

Identification Method of Influencing Factors of Hospital Catering Service Satisfaction Based on Decision Tree Algorithm

Liu

et al. 2022

Applied Bionics and Biomechanics

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“…After the texts have been changed into vectors, classification are performed. This study includes hyper-parametric classifiers from k-nearest neighbour (KNN) [ 17 ], Naïve Bayes (NB), AdaBoost (ADB), Decision tree (CART) [ 8 ], Random Forest (RF) [ 13 ], Support Vector Machine (SVM) [ 36 ], and Linear Regression Classifier (LR) [ 15 ].…”

Section: Experimental Results and Evaluationmentioning

confidence: 99%

Users opinion and emotion understanding in social media regarding COVID-19 vaccine

et al. 2022

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Online social platforms or social platforms such as Twitter, Facebook and Instagram have become popular platforms for a public discussion about social topics. Recent studies show that there is a growing tendency for people to talk about COVID-19 pandemic in these online channels. The rapid growth of the infected cases by COVID-19 pandemic makes a lots of anxiety and fear among people. With the recent released of Pfizer vaccine, people start posting a lot of rumors regarding the safety concerns of the vaccine, especially among the elderly people. The aim of this study is to bring out the fact that tweets containing all pertinent details about the COVID-19 vaccine and provides an analysis and understanding of users emotions regarding the recent release of COVID-19 vaccine. This study starts with the collection of tweets related to COVID-19 vaccine and then cleaning the dataset from redundant tweets. In this study, we use Twitter API and Web Scraping techniques to obtain a sample of 50,000 tweets talking about COVID-19 vaccine.Further, The analysis of users emotions is achieved by manually labeling and classifying the tweets to either positive or negative. Then, a deep learning based model is used to train the data and classify the people opinion about COVID-19 vaccine. The experimental results illustrate that high percentage of people have shown a positive attitude towards COVID1-19 vaccine. The proposed method is validated over Twitter datasets and the results also demonstrate that use of deep learning classifier can successfully improve the accuracy of people emotions analysis with an accuracy up to 98% for training set and the accuracy for testing set is 73%.

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“…Clarin conducted in-depth research on the safety management problems and countermeasures of foreign students in colleges and universities, expounded on the importance of early warning mechanisms, and established early warning mechanisms for emergencies, cultural conflicts, accidental injuries, and other problems endangering the personal safety of foreign students during their stay in China. The early warning mechanism can discover the causes of contradictions in advance, and managers can take measures in advance to reduce the impact scope of safety events and even avoid the occurrence of safety events, so as to achieve the transformation from remedying the occurrence of events to early warning to avoid the occurrence of events [ 7 ]. When exploring the management strategy of foreign students in China in the new era, Hammed and Jumoke mentioned the need to establish a prevention mechanism for emergencies of foreign students [ 8 , 9 ].…”

Section: Related Workmentioning

confidence: 99%

Early Warning of Enterprise Financial Risk Based on Decision Tree Algorithm

Hong¹,

Wu²,

Xü³

et al. 2022

Computational Intelligence and Neuroscience

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To improve enterprise financial early warning, we propose an algorithm based on a decision tree. According to the shortcomings and defects of the classical algorithm and the traditional decision tree algorithm, in the ordinary decision tree improved algorithm based on PCA, there is a problem that the representativeness of the data after dimensionality reduction processing are not high, resulting in the fact that the accuracy of the algorithm can be improved slightly after multiple data runs. Based on the classical algorithm, attribute eigenvalues before classification are extracted twice, and the amount of data to be classified is calculated. That is, the most important attributes of the original data are selected. After the subtree is established, the dimension reduction and merging selection of the data are performed, and the improved algorithm is verified by using three data sets in the UCI database. The results show that the average accuracy in the three datasets is 94.6%, which is improved by 1.6% and 0.6% for the traditional classical algorithm and the ordinary PCA decision tree optimization algorithm, respectively. PCA-based decision tree algorithms can improve the accuracy of the results to some extent, which is of practical importance. In the future, a classic algorithm improved for secondary modeling will be used to obtain a more efficient decision tree model. The decision tree algorithm has been proven to recognize an early warning of an enterprise’s financial risks, which enhances the effectiveness of an enterprise’s early financial warning.

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Academic Analytics: Predicting Success in the Licensure Examination of Graduates using CART Decision Tree Algorithm

Cited by 8 publications

References 0 publications

Identification Method of Influencing Factors of Hospital Catering Service Satisfaction Based on Decision Tree Algorithm

Identification Method of Influencing Factors of Hospital Catering Service Satisfaction Based on Decision Tree Algorithm

Users opinion and emotion understanding in social media regarding COVID-19 vaccine

Early Warning of Enterprise Financial Risk Based on Decision Tree Algorithm

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