scholarly journals CRDT: Correlation Ratio Based Decision Tree Model for Healthcare Data Mining

2016 ◽  
Author(s):  
Smita Roy ◽  
Samrat Mondal ◽  
Asif Ekbal ◽  
Maunendra Sankar Desarkar
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Decision Tree Model ◽  
Correlation Ratio ◽  
Tree Model ◽  
Healthcare Data

scholarly journals Predicting Paediatric Malaria Occurrence Using Classification Algorithm in Data Mining

2019 ◽  
pp. 1-10 ◽  
Author(s):  
T. C. Olayinka ◽  
S. C. Chiemeke
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Prediction Method ◽  
Developed Countries ◽  
Classification Algorithms ◽  
Data Sets ◽  
Tree Model ◽  
Healthcare Data ◽  
Paediatric Malaria ◽  
The Developed Countries

This paper gives the current overview of the application of data mining techniques on the haematological and biochemical dataset to predict the occurrence of malaria in children between age zero (0) and five (5).  Malaria has been eradicated from the developed countries but still affecting a large part of the world negatively. A larger percentage of malaria is estimated to affect young children in sub-Sahara Africa.  In order to reduce mortality from paediatric malaria, there should be an efficient and effective prediction method.  In healthcare, data mining is one of the most vital and motivating areas of research with the objective of finding meaningful information from huge data sets and provides an efficient analytical approach for detecting unknown and valuable information in healthcare data.  In this study, a model was built to predict the occurrence of malaria in children between age zero (0) and five (5) years, using decision tree classification algorithms on WEKA workbench tool.  The classification algorithms used are LMT, REPTree, Hoeffding tree and J48. A J48 algorithm was used for building the decision tree model since it has higher accuracy for performance with least error margin.

An Educational Data Mining Application by Using Multiple Intelligences

2020 ◽  
pp. 93-110
Author(s):  
Esra Aksoy ◽  
Serkan Narli ◽  
Mehmet Akif Aksoy
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Learning Styles ◽  
Multiple Intelligences ◽  
Gifted Students ◽  
Personality Types ◽  
Decision Tree Model ◽  
Tree Model ◽  
Educational Domain ◽  
Using Data

The aim of this chapter is to illustrate both uses of data mining methods and the way of these methods can be applied in education by using students' multiple intelligences. Data mining is a data analysis methodology that has been successfully used in different areas including the educational domain. In this context, in this study, an application of EDM will be illustrated by using multiple intelligence and some other variables (e.g., learning styles and personality types). The decision tree model was implemented using students' learning styles, multiple intelligences, and personality types to identify gifted students. The sample size was 735 middle school students. The constructed decision tree model with 70% validity revealed that examination of mathematically gifted students using data mining techniques may be possible if specific characteristics are included.

scholarly journals Customer Retention in Banking Sector using Decision Tree-Neuro Based System

2020 ◽  
Vol 9 (3) ◽  
pp. 481-485
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Banking Sector ◽  
Learning Algorithm ◽  
Decision Tree Model ◽  
Tree Model ◽  
Data Mining Techniques ◽  
Individual Decisions ◽  
Bank Marketing

All the bank marketing campaigns mostly deals with large amount of data. when they need to deal with huge electronic data of customers, then it is very difficult to analyze the data manually or by human analyst. Here comes the picture of data mining techniques to deal with the large amount of data and to come up with useful data which helps in decision making process. All the data mining techniques helps in analyzing the data. some of the techniques that can be used for this bank marketing campaigns are naive bayes, logistics regression technique and Decision tree model technique etc. among all these techniques decision Tree model gives the best solution in analyzing the human decisions. Artificial neural networks is a learning algorithm which learns from multiple individual decisions and their judgements, thus aggregates and generalizes the customers decision making knowledge.

Building a Decision Tree Model for Campus Information Score Based on the Algorithm C5.0

2015 ◽  
Vol 719-720 ◽  
pp. 805-811 ◽  
Author(s):  
Cong Lin Ran ◽  
Xiao Jing Wang
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Decision Tree Model ◽  
Utilization Rate ◽  
Network Technology ◽  
Tree Model ◽  
Weight Value ◽  
Information Score ◽  
Almost All ◽  
The Web

Network technology accelerates the development of educational information, campus portal building is considered as an important part of it in every university, almost all information of teaching and research appeared on the web. Meanwhile, the utilization rate of some websites was lower in university, information was updated slowly, information classifications were complex and not standardized on a platform. They didn’t emphasis on using and sharing but building and developing, and this phenomenon was widespread. So the paper proposed a decision tree model for score sorting information based on C5.0 algorithm, setting up a statistical model for data mining by adding a line weight value for portal information. Finally, the results verify the correctness and science of the model by giving an example.

scholarly journals Building Data Mining Decision Tree Model for Predicting Employee Performance

2019 ◽  
Vol 6 (2) ◽  
pp. 75-86
Author(s):  
Ira Mellisa
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Employee Performance ◽  
Decision Tree Model ◽  
Data Mining Technique ◽  
Tree Model ◽  
Mining Technique ◽  
High Dependency ◽  
Machine Operators ◽  
A Company

Human resource is one of the functions of a company that is considered as an asset. Therefo re, the theory of performance qualificat ion was adopted by the company in order to get an overview of employee performance. Furthermore, the company needs an effective method to predict the performance not only for the employees but also for the new applic ants. The goals of this research are to get a decision tree model of the employee performance. By learning employee data, the performance of the new applicants could be predicted. The study would provide the characteristic of new applicants who will give better performance than other applicants . The data from a company in Indonesia will have been used for this research. The data mining technique will be applied to the data of operators (such as admins, clerks, cashiers, machine operators, and security offi cers). The data mining technique was use d is decision tree. The decision tree technique was commonly used for a supervised learning data. The decision tree technique also has advantages compared others, because of its ability to produce information that is easy to understand. The result of this research shown the high dependency of employee performance with employment type (work contract). It also means that employees are encouraged to provide good performance to the company if those employees have become p ermanent employees. This research also showed that there is no relationship between employee performances with gender or position grade.

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