scholarly journals Healthcare data mining from multi-source data

2017 ◽  
Author(s):  
Ling Chen
Keyword(s):  
Data Mining ◽  
Healthcare Data ◽  
Source Data

The SPMF Open-Source Data Mining Library Version 2

2016 ◽  
pp. 36-40 ◽  
Author(s):  
Philippe Fournier-Viger ◽  
Jerry Chun-Wei Lin ◽  
Antonio Gomariz ◽  
Ted Gueniche ◽  
Azadeh Soltani ◽  
...  
Keyword(s):  
Data Mining ◽  
Open Source ◽  
Open Source Data ◽  
Source Data

Students' Learning Styles and Learning Effects

2014 ◽  
pp. 197-235
Keyword(s):  
Data Mining ◽  
High Schools ◽  
Learning Styles ◽  
Learning Style ◽  
Negative Association ◽  
Survey Result ◽  
Learning Style Inventory ◽  
Open Source Data ◽  
Source Data ◽  
Exam Scores

This chapter introduces the concept of learning style and Memletics learning style inventory, and uses open-source data mining software WEKA to cluster the students of experiment classes in four high schools according to the values of seven dimensions in the Memletics learning style inventory that are calculated based on the survey result about their learning styles. The clustering result demonstrates that verbal and physical are always positively associated with exam scores, visual dimension usually has negative association with score exams; the association of learning style with exam scores remains almost static, and the high, medium, and low sum of dimension values of learning style corresponds to high schools in developed, developing, and undeveloped area in China, respectively. The findings are analyzed. The implication of learning style for intelligent instruction of English subject as a foreign language is suggested.

Artificial Bee Colony-Based Associative Classifier for Healthcare Data Diagnosis

2021 ◽  
pp. 237-253
Author(s):  
M. Nandhini ◽  
S. N. Sivanandam ◽  
S. Renugadevi
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Artificial Bee Colony ◽  
Optimization Technique ◽  
Rule Mining ◽  
Healthcare Data ◽  
Bee Colony ◽  
Small Set ◽  
Significant Class

Data mining is likely to explore hidden patterns from the huge quantity of data and provides a way of analyzing and categorizing the data. Associative classification (AC) is an integration of two data mining tasks, association rule mining, and classification which is used to classify the unknown data. Though association rule mining techniques are successfully utilized to construct classifiers, it lacks in generating a small set of significant class association rules (CARs) to build an accurate associative classifier. In this work, an attempt is made to generate significant CARs using Artificial Bee Colony (ABC) algorithm, an optimization technique to construct an efficient associative classifier. Associative classifier, thus built using ABC discovered CARs achieve high prognostic accurateness and interestingness value. Promising results were provided by the ABC based AC when experiments were conducted using health care datasets from the UCI machine learning repository.

For Better Healthcare Mining Health Data

2017 ◽  
pp. 135-158
Author(s):  
Güney Gürsel
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Healthcare Management ◽  
Support Vector ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Management Fraud ◽  
Use Of Data ◽  
Customer Relation Management ◽  
Vector Machines

Data mining has great contributions to the healthcare such as support for effective treatment, healthcare management, customer relation management, fraud and abuse detection and decision making. The common data mining methods used in healthcare are Artificial Neural Network, Decision trees, Genetic Algorithms, Nearest neighbor method, Logistic regression, Fuzzy logic, Fuzzy based Neural Networks, Bayesian Networks and Support Vector Machines. The most used task is classification. Because of the complexity and toughness of medical domain, data mining is not an easy task to accomplish. In addition, privacy and security of patient data is a big issue to deal with because of the sensitivity of healthcare data. There exist additional serious challenges. This chapter is a descriptive study aimed to provide an acquaintance to data mining and its usage and applications in healthcare domain. The use of Data mining in healthcare informatics and challenges will be examined.

scholarly journals Data Mining Risk Score Models for Big Biomedical and Healthcare Data

2015 ◽  
Vol 08 (06) ◽  
Author(s):  
Emad Elsebakhi ◽  
Ognian Asparouhov ◽  
Anton Berisha
Keyword(s):  
Data Mining ◽  
Risk Score ◽  
Healthcare Data

Arithmetic Research on Data Mining Technology and Associative Rules Mining

2014 ◽  
Vol 556-562 ◽  
pp. 3949-3951
Author(s):  
Jian Xin Zhu
Keyword(s):  
Data Mining ◽  
High Performance ◽  
Knowledge Engineering ◽  
Business Decisions ◽  
Depth Data ◽  
Source Data ◽  
Wide Availability ◽  
High Level ◽  
Performance Computing ◽  
Level Analysis

Data mining is a technique that aims to analyze and understand large source data reveal knowledge hidden in the data. It has been viewed as an important evolution in information processing. Why there have been more attentions to it from researchers or businessmen is due to the wide availability of huge amounts of data and imminent needs for turning such data into valuable information. During the past decade or over, the concepts and techniques on data mining have been presented, and some of them have been discussed in higher levels for the last few years. Data mining involves an integration of techniques from database, artificial intelligence, machine learning, statistics, knowledge engineering, object-oriented method, information retrieval, high-performance computing and visualization. Essentially, data mining is high-level analysis technology and it has a strong purpose for business profiting. Unlike OLTP applications, data mining should provide in-depth data analysis and the supports for business decisions.

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