Finding boundary subjects for medical decision support with support vector machines

2003 ◽  
Author(s):  
D. Rebernak ◽  
M. Lenic ◽  
P. Kokol ◽  
V. Zumer
Keyword(s):  
Decision Support ◽  
Support Vector Machines ◽  
Medical Decision ◽  
Support Vector ◽  
Medical Decision Support ◽  
Vector Machines

Decision Support with Support Vector Machinesin R & D Project Termination Decision

2011 ◽  
Vol 403-408 ◽  
pp. 4098-4102
Author(s):  
Jing Rong Dong ◽  
Yu Ke Chen
Keyword(s):  
Reinforcement Learning ◽  
Decision Support ◽  
Project Management ◽  
Statistical Analysis ◽  
Support Vector Machines ◽  
Evaluation Criteria ◽  
Support Vector ◽  
Selection Decisions ◽  
Vector Machines ◽  
Termination Decisions

Research and development (R&D) project termination decision is an important and challenging task for organizations with R&D project management .Current research on R&D project management mainly focuses on project selection decisions. Very little research has been done on the termination decision of R&D projects .In this paper a support vector machines classifer for assisting managers in deciding whether to abandon an ongoing R&D project at various stages of R&D is presented. It has also shown by the modeling and pattern recognizing results in terms of termination decisions of fifty R&D projects that the method possesses reinforcement learning properties and universalized capabilities. With respect to modeling and termination decision of R&D project, which has the fact that the evaluation criteria are hardly ever determined by conventional approaches such as statistical analysis, the method is available.

scholarly journals Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs)

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Robert A. Sowah ◽  
Marcellinus Kuuboore ◽  
Abdul Ofoli ◽  
Samuel Kwofie ◽  
Louis Asiedu ◽  
...  
Keyword(s):  
Health Insurance ◽  
Decision Support ◽  
Support Vector Machines ◽  
Service Providers ◽  
National Health Insurance Scheme ◽  
Support Vector ◽  
Insurance Claims ◽  
Insurance Scheme ◽  
Vector Machines ◽  
Health Insurance Claims

Fraud in health insurance claims has become a significant problem whose rampant growth has deeply affected the global delivery of health services. In addition to financial losses incurred, patients who genuinely need medical care suffer because service providers are not paid on time as a result of delays in the manual vetting of their claims and are therefore unwilling to continue offering their services. Health insurance claims fraud is committed through service providers, insurance subscribers, and insurance companies. The need for the development of a decision support system (DSS) for accurate, automated claim processing to offset the attendant challenges faced by the National Health Insurance Scheme cannot be overstated. This paper utilized the National Health Insurance Scheme claims dataset obtained from hospitals in Ghana for detecting health insurance fraud and other anomalies. Genetic support vector machines (GSVMs), a novel hybridized data mining and statistical machine learning tool, which provide a set of sophisticated algorithms for the automatic detection of fraudulent claims in these health insurance databases are used. The experimental results have proven that the GSVM possessed better detection and classification performance when applied using SVM kernel classifiers. Three GSVM classifiers were evaluated and their results compared. Experimental results show a significant reduction in computational time on claims processing while increasing classification accuracy via the various SVM classifiers (linear (80.67%), polynomial (81.22%), and radial basis function (RBF) kernel (87.91%).

Topological applications of multilayer perceptrons and support vector machines in financial decision support systems

2018 ◽  
Vol 24 (1) ◽  
pp. 474-507 ◽  
Author(s):  
Mohammad Zoynul Abedin ◽  
Chi Guotai ◽  
Fahmida-E- Moula ◽  
A.S.M. Sohel Azad ◽  
Mohammed Shamim Uddin Khan
Keyword(s):  
Decision Support ◽  
Support Vector Machines ◽  
Decision Support Systems ◽  
Support Systems ◽  
Support Vector ◽  
Multilayer Perceptrons ◽  
Vector Machines ◽  
Financial Decision
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