C linical prediction rules (CPRs) are tools designed to improve decision making in clinical practice by assisting practitioners in making a particular diagnosis, establishing a prognosis, or matching patients to optimal interventions based on a parsimonious subset of predictor variables from the history and physical examination.1,2 Clinical prediction rules have been developed to improve decisi...

This paper presents new approaches to formulating and solving complex real-life decision-making problems, making use of the creativity concept. We assume that the decision-making process is embedded in the system of views and mutual relations between the decision-makers and their surrounding environment, so that creativity, as defined formally in Sec. 2, could play a primary role in the decisio...

A decision is often made in a group and decision makers often utilize fuzzy judgments in attempting to reach an optimal solution. In order to deal with the fuzziness of decision makers' judgments in solutions, this paper proposes a fuzzy group decision-making method for multi-objective decision problems. The method allows group members to express their fuzzy preferences for decision objectives,...

We present a dynamic model of sequential information acquisition by a heterogeneous committee. At each date agents decide whether to continue deliberation, generating costly information, or stop and take a binding vote yielding a decision. For homogeneous committees, the model is a reinterpretation of the classic Wald (1947) sequential testing of statistical hypotheses. In heterogeneous committ...

In this article, the specification of a histopathology decision making support system, based on Pawlak’s information system concept and Mamdani type fuzzy control is presented. The proposed system supports the recognition process of HER-2/neu histopathology preparations through microscopy image information analysis. We used Pawlak’s information system to identify the decisive set of features an...

This paper presents a data envelopment analysis (DEA) model combined with Bootstrapping to assess performance of one of the Data mining Algorithms. We applied a two-step process for performance productivity analysis of insurance branches within a case study. First, using a DEA model, the study analyzes the productivity of eighteen decision-making units (DMUs). Using a Malmquist index, DEA deter...

There are many methods introduced to solve the credit scoring problem such as support vector machines, neural networks and rule based classifiers. Rule bases are more favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants.In this paper multi-objective particle swarm is applied to optimize fuzzy apriori rule base in credit scoring. ...

This study differs from previous ones applying multivariate statistical analysis and multiple criteria decision-making (MCDM) methods. We use the Variable Consistency Dominance-based Rough Set Approach (VC-DRSA) to formulate airline service strategies by generating airline service decision rules thatmodelpassengerpreferences for airline servicequality. Flowgraphsare applied to inferdecision rul...

Decision making has been traditionally based on a managers experience. This paper, however, discusses how a software project simulator based on System Dynamics and Evolutionary Computation can be combined to obtain management rules. The purpose is to provide accurate decision rules to help project managers to make decisions at any time in the software development life cycle. To do so, a databas...