scholarly journals A Literature Survey on Data Mining Approach to Effectively Handle Cancer Treatment

2018 ◽  
Vol 7 (2.7) ◽  
pp. 729 ◽  
Author(s):  
Sri Hari Nallamala ◽  
Siva Kumar Pathuri ◽  
Dr Suvarna Vani Koneru
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Cancer Treatment ◽  
User Interaction ◽  
Medical Practitioner ◽  
Vital Role ◽  
Healthcare Management ◽  
Clinical Approach ◽  
Internet Resource

The effective treatment of cancer is not very easy since diagnosis of cancer involves many stages of treatment with gradually changing lifestyles. Physicians play vital role in identifying the correct cause and feel ambiguity for making perfect decisions about hundreds of data available from the internet resource. IDA (Intelligent Data Analysis) which is a part from Data Mining techniques is quiet useful to most of the physicians for decision making about types of cancers. IDA facilitates physicians to classify, detect and analyze the cancer outcome to patients. Healthcare Management System also aids the practitioners to practically search, analyze and compare the result analysis of the patient with existing data in the HMS and guide proper treatment to the cancer affected patient. Health care data analysis comprises enormous data with diversity of health information. One among the most important points that pull down the practitioner’s confidence is that utility of latest software and most sophisticated computing machines. This put them in to the state of confusion for proper and elegant decision making for treating the cancer affected patients. Problems in user interaction, lack of awareness in data mining, improper knowledge in electronic guidelines makes physicians to work with old methods of treatment. Traditional medical practicing and modern methods of computing do not match either because of ignorance. IDA and HMS have significant impact for cancer treatment with speedy diagnosis and faster recovery. This also shows great impact on costs, clinical outcomes and proper guidelines for clinical approach. The prime motto of this survey article is to analyze the survey application, bring out the importance of comparison strategies of IDA to improve decision making for medical practitioner for effective cancer treatment.  

Integrating OLAP/SOLAP in E-Business Domains

2011 ◽  
Vol 24 (3) ◽  
pp. 45-60
Author(s):  
Ben Ali ◽  
Samar Mouakket
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
United Arab Emirates ◽  
Decision Makers ◽  
Job Seekers ◽  
Job Market ◽  
Analysis Techniques ◽  
Analytical Processing ◽  
User Friendly

E-business domains have been considered killer domains for different data analysis techniques. Most researchers have examined data mining (DM) techniques to analyze the databases behind E-business websites. DM has shown interesting results, but this technique presents some restrictions concerning the content of the database and the level of expertise of the users interpreting the results. In this paper, the authors show that successful and more sophisticated results can be obtained using other analysis techniques, such as Online Analytical Processing (OLAP) and Spatial OLAP (SOLAP). Thus, the authors propose a framework that fuses or integrates OLAP with SOLAP techniques in an E-business domain to perform easier and more user-friendly data analysis (non-spatial and spatial) and improve decision making. In addition, the authors apply the framework to an E-business website related to online job seekers in the United Arab Emirates (UAE). The results can be used effectively by decision makers to make crucial decisions in the job market of the UAE.

Symbolic Data Clustering

2011 ◽  
pp. 1087-1091 ◽  
Author(s):  
Edwin Diday ◽  
M. Narasimha Murthy
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Internal Structure ◽  
Data Clustering ◽  
Large Datasets ◽  
Complex Data ◽  
Symbolic Data Analysis ◽  
New Techniques ◽  
Symbolic Data

In data mining, we generate class/cluster models from large datasets. Symbolic Data Analysis (SDA) is a powerful tool that permits dealing with complex data (Diday, 1988) where a combination of variables and logical and hierarchical relationships among them are used. Such a view permits us to deal with data at a conceptual level, and as a consequence, SDA is ideally suited for data mining. Symbolic data have their own internal structure that necessitates the need for new techniques that generally differ from the ones used on conventional data (Billard & Diday, 2003). Clustering generates abstractions that can be used in a variety of decision-making applications (Jain, Murty, & Flynn, 1999). In this article, we deal with the application of clustering to SDA.

scholarly journals Data Analysis and Data Mining: Current Issues in Biomedical Informatics

2011 ◽  
Vol 50 (06) ◽  
pp. 536-544 ◽  
Author(s):  
M. Diomidous ◽  
I. N. Sarkar ◽  
K. Takabayashi ◽  
A. Ziegler ◽  
A. T. McCray ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Knowledge Integration ◽  
Biomedical Informatics ◽  
Data Driven ◽  
Biomedical Data ◽  
Translational Bioinformatics ◽  
Biomedical Sciences ◽  
Mining Methods

SummaryBackground: Medicine and biomedical sciences have become data-intensive fields, which, at the same time, enable the application of data-driven approaches and require sophisticated data analysis and data mining methods. Biomedical informatics provides a proper interdisciplinary context to integrate data and knowledge when processing available information, with the aim of giving effective decision-making support in clinics and translational research.Objectives: To reflect on different perspectives related to the role of data analysis and data mining in biomedical informatics. Methods: On the occasion of the 50th year of Methods of Information in Medicine a symposium was organized, which reflected on opportunities, challenges and priorities of organizing, representing and analysing data, information and knowledge in biomedicine and health care. The contributions of experts with a variety of backgrounds in the area of biomedical data analysis have been collected as one outcome of this symposium, in order to provide a broad, though coherent, overview of some of the most interesting aspects of the field.Results: The paper presents sections on data accumulation and data-driven approaches in medical informatics, data and knowledge integration, statistical issues for the evaluation of data mining models, translational bioinformatics and bioinformatics aspects of genetic epidemiology.Conclusions: Biomedical informatics represents a natural framework to properly and effectively apply data analysis and data mining methods in a decision-making context. In the future, it will be necessary to preserve the inclusive nature of the field and to foster an increasing sharing of data and methods between researchers.

scholarly journals Data mining as a tool in privacy-preserving data publishing

2010 ◽  
Vol 45 (1) ◽  
pp. 151-159 ◽  
Author(s):  
Michal Sramka
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Sensitive Information ◽  
Private Data ◽  
Privacy Preserving Data Publishing

ABSTRACTMany databases contain data about individuals that are valuable for research, marketing, and decision making. Sharing or publishing data about individuals is however prone to privacy attacks, breaches, and disclosures. The concern here is about individuals’ privacy-keeping the sensitive information about individuals private to them. Data mining in this setting has been shown to be a powerful tool to breach privacy and make disclosures. In contrast, data mining can be also used in practice to aid data owners in their decision on how to share and publish their databases. We present and discuss the role and uses of data mining in these scenarios and also briefly discuss other approaches to private data analysis.

Creating Competitive Advantage by Using Data Mining Technique as an Innovative Method for Decision Making Process in Business

2013 ◽  
pp. 205-213
Author(s):  
Mert Bal ◽  
Yasemin Bal ◽  
Ayse Demirhan
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Competitive Advantage ◽  
Decision Making Process ◽  
Success Factor ◽  
Data Sets ◽  
Data Mining Technique ◽  
Critical Success ◽  
Using Data

Competitive advantage is at the heart of a firm’s performance in today’s challenging and rapidly changing environment. One of the central bases for achieving competitive advantage is the organizational capability to create new knowledge and transfer it across various levels of the organization. Traditional methods of data analysis, based mainly on human dealing directly with the data, simply do not scale to handle with large data sets. This explosive growth in data and databases has generated an urgent need for new techniques and tools that can intelligently and automatically transform the processed data into useful information and knowledge. Consequently, data mining has become a research area with increasing importance. Organizations of all sizes have started to develop and deploy data mining technologies to leverage data resources to enhance their decision making capabilities. Business information received from data analysis and data mining is a critical success factor for companies wishing to maximize competitive advantage. In this study, the importance of gaining knowledge for organizations in today’s competitive environment are discussed and data mining method in decision making process is analyzed as an innovative technique for organizations.

scholarly journals Bridging the divide: the adjustment and decision-making experiences of people with dementia living with a recent diagnosis of cancer and its impact on family carers

2018 ◽  
Vol 40 (5) ◽  
pp. 944-965 ◽  
Author(s):  
Lorna McWilliams ◽  
Caroline Swarbrick ◽  
Janelle Yorke ◽  
Lorraine Burgess ◽  
Carole Farrell ◽  
...  
Keyword(s):  
Decision Making ◽  
Cancer Treatment ◽  
Treatment Options ◽  
Treatment Decision ◽  
Vital Role ◽  
Family Carers ◽  
Structured Interviews ◽  
Cancer Symptoms ◽  
People With Dementia ◽  
Diagnosis Of Cancer

AbstractThe risk of living with dementia and, separately, cancer, increases exponentially with age. However, to date, there is a paucity of research investigating the experiences of people living with both these conditions. This study used semi-structured interviews to explore the decision-making and treatment options for people who live with both dementia and cancer. In total, ten people living with both dementia and cancer (aged 39–93 years) and nine family carers were interviewed. Braun and Clarke's approach to thematic analysis was used together with framework matrices to organise the data. In this article four sequential and descriptive themes are presented. ‘Reaching a diagnosis of cancer’ describes the vital role that family carers play in encouraging the person with dementia to seek an explanation for their presenting (undiagnosed cancer) symptoms to their general practitioner. ‘Adjusting to the cancer diagnosis when living with dementia’ outlines a variety of emotional and practical responses to receiving news of the diagnosis. ‘Weighing up the cancer treatment options’ highlights the different decisions and circumstances that family carers and people living with both dementia and cancer are faced with post-diagnosis. ‘Undergoing cancer treatment’ shares the finding that cancer treatment decision-making was not straightforward and that people living with both dementia and cancer would often forget about their cancer and what procedures they had been through.

scholarly journals Study of The ID3 and C4.5 Learning Algorithms

2020 ◽  
Vol 1 (2) ◽  
pp. 29-43
Author(s):  
Y. Fakir ◽  
M. Azalmad ◽  
R. Elaychi
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Decision Tree ◽  
Decision Trees ◽  
Information Gain ◽  
Learning Algorithms ◽  
Large Data ◽  
Classification Algorithms ◽  
Important Data

Data Mining is a process of exploring against large data to find patterns in decision-making. One of the techniques in decision-making is classification. Data classification is a form of data analysis used to extract models describing important data classes. There are many classification algorithms. Each classifier encompasses some algorithms in order to classify object into predefined classes. Decision Tree is one such important technique, which builds a tree structure by incrementally breaking down the datasets in smaller subsets. Decision Trees can be implemented by using popular algorithms such as ID3, C4.5 and CART etc. The present study considers ID3 and C4.5 algorithms to build a decision tree by using the “entropy” and “information gain” measures that are the basics components behind the construction of a classifier model

Creating Competitive Advantage by Using Data Mining Technique as an Innovative Method for Decision Making Process in Business

2011 ◽  
Vol 1 (3) ◽  
pp. 38-45 ◽  
Author(s):  
Mert Bal ◽  
Yasemin Bal ◽  
Ayse Demirhan
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Competitive Advantage ◽  
Large Data ◽  
Research Area ◽  
Decision Making Process ◽  
Success Factor ◽  
Organizational Capability ◽  
Data Mining Technique

Competitive advantage is at the heart of a firm’s performance in today’s challenging and rapidly changing environment. One of the central bases for achieving competitive advantage is the organizational capability to create new knowledge and transfer it across various levels of the organization. Traditional methods of data analysis, based mainly on human dealing directly with the data, simply do not scale to handle with large data sets. This explosive growth in data and databases has generated an urgent need for new techniques and tools that can intelligently and automatically transform the processed data into useful information and knowledge. Consequently, data mining has become a research area with increasing importance. Organizations of all sizes have started to develop and deploy data mining technologies to leverage data resources to enhance their decision making capabilities. Business information received from data analysis and data mining is a critical success factor for companies wishing to maximize competitive advantage. In this study, the importance of gaining knowledge for organizations in today’s competitive environment are discussed and data mining method in decision making process is analyzed as an innovative technique for organizations.

scholarly journals Implementasi Data Warehouse dan Data Mining: Studi Kasus Analisis Peminatan Studi Siswa

2011 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Eka Miranda
Keyword(s):  
Data Mining ◽  
Academic Achievement ◽  
Decision Making ◽  
Data Analysis ◽  
Data Warehouse ◽  
Ad Hoc ◽  
Data Distribution ◽  
Program Selection ◽  
Mining Design ◽  
The University

This paper discusses the implementation of data mining and their role in helping decision-making related to students’ specialization program selection. Currently, the university uses a database to store records of transactions which can not directly be used to assist analysis and decision making. Based on these issues then made the data warehouse design used to store large amounts of data and also has the potential to gain new data distribution perspectives and allows to answer the ad hoc question as well as to perform data analysis. The method used consists of: record analysis related to students’ academic achievement, designing data warehouse and data mining. The paper’s results are in a form of data warehouse and data mining design and its implementation with the classification techniques and association rules. From these results can be seen the students’ tendency and pattern background in choosing the specialization, to help them make decisions. 

scholarly journals Data Mining and Knowledge Discovery in Big Data for Decision Making in Higher Education

2021 ◽  
Vol 14 (4) ◽  
pp. 2013-2019
Author(s):  
Hanna Mohammad Said
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Knowledge Discovery ◽  
Real World ◽  
Rapid Development ◽  
Teaching Quality ◽  
World Knowledge

Artificial intelligence and data mining plays a fundamental role in improving the intelligence of education through special standards for improving teaching quality, better learning experience, predictive teaching, assessment method, effective decision-making, and improved data analysis. BD (Big Data) are also used to assess, detect, and anticipate decision-making, failure risk, and consequences to improve decision-making and maintain high-quality standards. According to the findings of this study, certain universities and governments have adopted BD to help students transition from traditional to smart digital education. Many obstacles remain in the way of complete adoption, including security, privacy, ethics, a scarcity of qualified specialists, data processing, storage, and interoperability. Learning today is getting smarter, thanks to the rapid development of the use of data and knowledge for big data analysis. Besides delivering real-world knowledge discovery applications, specialized data mining methodologies, and obstacles have real-world applications. Therefore, this article aims to explain the current concept of an intelligent learning environment in higher education. It explores the main criteria, and presents evaluation methods through the use of the proposed model.

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