March 12, 2015 -- The contents of the latest issue of: International Journal of Decision Support System Technology (IJDSST) Volume 6, Issue 4, October - December 2014 are summarized. IJDSST is published quarterly in print and electronically with ISSN: 1941-6296; EISSN: 1941-630X. The journal is published by IGI Global Publishing, Hershey, USA (www.igi-global.com/ijdsst). The Editor-in-Chief is Pascale Zaraté (Toulouse University, France).

Note: There are no submission or acceptance fees for manuscripts submitted to the International Journal of Decision Support System Technology (IJDSST). All manuscripts are accepted based on a double-blind peer review editorial process.

ARTICLE 1

A New Approach for Coronary Artery Diseases Diagnosis Based on Genetic Algorithm

Sidahmed Mokeddem (Department of Informatics, University of Oran, Oran, Algeria), Baghdad Atmani (Computer Science Department, University of Oran, Oran, Algeria), Mostéfa Mokaddem (Computer Science Department, University of Oran, Oran, Algeria)

Feature Selection (FS) has become the motivation of much research on decision support systems areas for which datasets with large number of features are analyzed. This paper presents a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic Algorithm (GA) wrapper Bayes Naïve (BN). Initially, thirteen attributes were involved in predicting CAD. In GA–BN algorithm, GA produces in each iteration a subset of attributes that will be evaluated using the BN in the second step of the selection procedure. The final result set of attribute holds the most pertinent feature model that increases the accuracy. The accuracy results showed that the algorithm produces 85.50% classification accuracy in the diagnosis of CAD. Therefore, the strength of the Algorithm is then compared with other machine learning algorithms such as Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are respectively 83.5%, 83.16% and 80.85%. Then, the GA wrapper BN Algorithm is similarly compared with other FS algorithms. The Obtained results have shown very favorable outcomes for the diagnosis of CAD.

ARTICLE 2

Quantitative Concession Behavior Analysis and Prediction for Decision Support in Electronic Negotiations

Réal A. Carbonneau (InterNeg Research Centre, Concordia University, Montreal, Canada), Rustam Vahidov (Department of Supply Chain & Business Technology Management, Concordia University, Montreal, Canada), Gregory E. Kersten (Department of Supply Chain & Business Technology Management, Concordia University, Montreal, Canada)

Quantitative analysis of negotiation concession behavior is performed based on empirical data with the purpose of providing simple and intuitive decision support in electronic negotiations. Previous work on non-linear concave preferences and subsequent concession crossover provides a theoretical basis for the model. The authors propose a model which quantifies the remaining concession potential for each issue and a generalization of the model which permits the memory/decay of past concessions. These models permit the analysis of negotiators' concession behavior. Using the proposed models, it was possible to quantitatively determine that negotiators in the authors' negotiation case exhibit concession crossover issues and thus have a tendency to give concessions on issues with the most remaining concession potential. This finding provides empirical evidence of concession crossover in actual concessions and the corresponding model permits the design of a simple and intuitive prediction methodology, which could be used in real world negotiations by decision support systems or automated negotiation agents.

ARTICLE 3

A Predictive E-Health Information System: Diagnosing Diabetes Mellitus Using Neural Network Based Decision Support System

Ahmad Al-Khasawneh (Hashemite University, Zarqa, Jordan), Haneen Hijazi (Hashemite University, Zarqa, Jordan)

Diabetes Mellitus is a chronic disease and a major cause of several severe complications and death in both developing and developed countries. The number of diabetes cases world-wide has been climbed up drastically over last decades. Hence, it was of utmost important to manage this illness and to develop tools that help clinicians do their job professionally. Artificial neural networks play a major role herein. In this research, a clinical decision support system that helps in diagnosing diabetes has been developed. The system was implemented using a multilayer perceptron artificial neural network. Due to the fact that there is no systematic way to follow in order to determine the number of hidden layers and neurons in MLP, an algorithm was proposed and followed based on the rules-of-thumb previously defined around this issue. As a result, two different topologies were trained and verified using cross validation technique. The topology that exhibited the best averaged accuracy was that of one hidden layer. The data set was obtained from King Abdullah University Hospital in Jordan.

ARTICLE 4

Issues and Strategies for Group and Negotiation Support Systems Research

Graham Peter Pervan (School of Information Systems, Curtin University, Perth, Australia), David Arnott (Decision Support Systems Laboratory, Monash University, Melbourne, Australia)

This research project was principally motivated by a concern for the direction and relevance of research in systems that support group work and negotiation. The main areas of research focus are the publication frequency and outlets for GSS and NSS research, the research strategies used in published articles, and the professional relevance of the research. The project has analysed 383 GSS articles and 82 NSS articles published in 16 major journals from 1990 to 2010. The findings indicate a significant dependence on the Journal of Group Decision and Negotiation but represent an opportunity for newer journals such as the International Journal of Decision Support Systems Technology. Other issues include a focus on experimental research and design science, weak theoretical foundations and research methodologies, and a focus on operational level problems. Of great concern is the finding that GSS and NSS research has relatively low professional and managerial relevance. Eight key strategies for dealing with these issues are recommended.

Interested authors should consult the journal's manuscript submission guidelines www.igi-global.com/calls-for-papers/international-journal-decision-support-system/1120