In today’s economy the importance of effective and efficient decision making has become
increasingly important in order to stay competitive in a global market set.
Obtaining most relevant data and outputs is the key for best decisions on every management
level. That is why the application of decision support systems (DSS) is now irreplaceable in
organizations that operate a state-of-the art decision making processes.
This paper discusses Group Support Systems, Expert Systems, Knowledge Management
Systems, Neural Networks, and Fuzzy Logic Systems focusing on
· the description of the methodology
· their applications and software vendors
· current research
· and new developments.
The analysis of these five systems is followed up by a case study that provides an example for
a real life application of a neural network. It will be exposed how this decision support system
facilitates the classification of potential patients for hospitals and how effective marketing
strategies specified to each group of patients could be created.
By comparison the application of an expert system for the same problem will be investigated.
Concluding, a summary will compose the results of this paper focusing particularly on the
outcomes of the case study. Advantages/disadvantages for the application of either system
will be examined. The authors will then eventually state their personal recommendation for
solving the problem given in the case study and comment on possible reasons why hospitals
decided to apply a neural network.
Table of Contents
1 Abstract
2 Technologies used by decision-makers in Decision Support Systems and Intelligent Systems
2.1 Group Support Systems (GSS)
2.1.1 Description of GSS Methodology
2.1.2 Applications and Software Vendors
2.1.3 Current Research Areas and New Developments
2.2 Expert Systems (ES)
2.2.1 Description of ES Methodology
2.2.2 Applications and Software Vendors
2.2.3 Current Research Areas and New Developments
2.3 Knowledge Management System (KMS)
2.3.1 Description of KMS Methodology
2.3.2 Applications and Software Vendors
2.3.3 Current Research Areas and New Developments
2.4 Artificial Neural Networks (ANN)
2.4.1 Description of ANN Methodology
2.4.2 Applications and Software Vendors
2.4.3 Current Research Areas and New Developments
2.5 Fuzzy Logic (FL)
2.5.1 Description of FL Methodology
2.5.2 Applications and Software Vendors
2.5.3 Current Research Areas and New Developments
3 Implementation Case Study for ANN Technology: Taiwanese Hospitals
3.1 Case Study: Consumers’ Behaviour in Choosing Hospitals
3.2 Problem Identification & Description
3.3 Solution of the Problem in the Case Study: ANN
3.4 Alternative Solution: Using an ES
3.5 Comparison and Recommendations
3.5.1 Critical Comparison of ANN and ES solution
3.5.2 Table Summarising Findings
3.5.3 Justification of the Organisation’s Decision
3.5.4 Recommendation
4 A Short Conclusion
Objectives and Research Themes
The primary objective of this coursework is to analyze five contemporary Decision Support System (DSS) technologies, detailing their methodologies, applications, and current research, while evaluating their effectiveness through a practical case study concerning hospital patient segmentation in Taiwan.
- Technical overview of Group Support Systems, Expert Systems, Knowledge Management Systems, Neural Networks, and Fuzzy Logic.
- Evaluation of artificial intelligence applications in the healthcare sector.
- Comparative analysis of Artificial Neural Networks (ANN) versus Expert Systems (ES) for classification tasks.
- Investigation into how data-driven consumer behavior patterns can optimize hospital marketing strategies.
Excerpt from the Book
3.3 Solution of the Problem in the Case Study: ANN
To better understand consumers’ choices a classification of them on basis of the demographic data and more importantly, on basis of their responses in the questionnaire is very helpful. The authors of the presented case study Lee, Shih, & Chung (2008) choose to use an ANN to gain a better insight into consumers’ minds, as ANNs are inherently non-linear models that are capable of recognising unknown functional relationships and are not constrained by any mathematical predefined functional form. Therefore they regard them as well suited to solve this classification problem.
Haykin (1999) says that feed-forward multilayer networks have the biggest success in solving classification problems (minimal error, maximal classification). Therefore Lee, Shih, & Chung (2008) use a feed-forward multilayer backpropagation model in their research. 80% of the subjects in the dataset are used to train the ANN, while 20% are used as testing subjects.
The software tool they use to create the ANN is MATLAB 6.0. MATLAB 6.0 offers a toolbox of neural network modelling tools and moreover offers its users a function to convert ANN into application programs, so that the ANN can be integrated into management information systems (MIS) of hospitals. After trying ANNs with various numbers of layers and processing elements, and different learning momentums and evaluating the convergence of the results to the expected data by using the root mean square error, it was decided to use a model with two hidden layers and 20 hidden nodes.
Summary of Chapters
1 Abstract: Provides an overview of the importance of effective decision making and outlines the scope of the paper, covering five key decision support technologies and their application in a hospital case study.
2 Technologies used by decision-makers in Decision Support Systems and Intelligent Systems: Detailed examination of Group Support Systems, Expert Systems, Knowledge Management Systems, Artificial Neural Networks, and Fuzzy Logic, focusing on their methodologies, market applications, and research trends.
3 Implementation Case Study for ANN Technology: Taiwanese Hospitals: Presents a real-world application of ANN for classifying hospital consumers based on demographic and survey data, including a comparative assessment against Expert Systems.
4 A Short Conclusion: Summarizes the analytical findings and reinforces the importance of organizational technology assessment for successful decision support integration.
Keywords
Decision Support Systems, Artificial Neural Networks, Expert Systems, Group Support Systems, Knowledge Management, Fuzzy Logic, Consumer Behavior, Hospital Management, Data Classification, Marketing Strategy, Artificial Intelligence, Business Intelligence, Patient Segmentation, Machine Learning, Information Technology.
Frequently Asked Questions
What is the core focus of this research paper?
This paper examines various decision support technologies to determine their potential in aiding management decisions, specifically highlighting their utility through a case study in the healthcare sector.
Which specific decision support technologies are analyzed?
The paper covers Group Support Systems (GSS), Expert Systems (ES), Knowledge Management Systems (KMS), Artificial Neural Networks (ANN), and Fuzzy Logic (FL).
What is the primary goal of the case study presented?
The study aims to demonstrate how Artificial Neural Networks can be used to classify hospital patients into specific segments based on their preferences, allowing hospitals to create targeted marketing strategies.
What research methodology is employed in this work?
The work combines a literature review of decision support system methodologies with a critical comparison of ANN and ES performance, applied to a dataset of Taiwanese hospital patients.
What topics are covered in the main section of the paper?
The main sections detail the technical methodologies of each system, their applications in industry, and a deep-dive analysis comparing ANN and ES in the context of consumer behavior research.
Which keywords best describe this study?
Key concepts include Decision Support Systems, Artificial Neural Networks, Expert Systems, Consumer Behavior, and Healthcare Management.
Why are Artificial Neural Networks preferred over Expert Systems in the case study?
ANNs are favored for their ability to handle large, complex datasets, their capacity to learn non-linear relationships without manual rule-programming, and their cost-efficiency compared to the expert-intensive nature of Expert Systems.
What limitation is noted regarding the use of ANNs in the case study?
A significant limitation is the ANN's requirement for complete data; the paper notes that incomplete questionnaires cannot be utilized, leading to recommendations for better data collection incentives.
How does the author suggest improving the decision-making process for hospitals?
The author proposes a hybrid system integrating ANNs for initial data analysis and Expert Systems for more specific qualitative analysis, alongside efforts to ensure high-quality, complete survey data collection.
- Quote paper
- Karl Grajczyk (Author), 2008, Designing and Exploring Intelligent Decision Support Systems , Munich, GRIN Verlag, https://www.grin.com/document/142203
