Today in field of Medicine there is a bulk collections and sets of data in hand about the patients, diagnosis, treatment procedures etc .Using data mining techniques in those cases endow with a statistical and logical analysis of the data looking for patterns that can aid by decision making and prediction. In this paper we presents study of using Bayesian network (BN) in the domain of cancer sp...

In this paper, we present an Internet-based, Bayesian Decision Network engine to aid watershed stakeholders in collaborative decision-making. Recent years have seen an increased emphasis on including all affected parties in the process of making water resources and water quality management decisions (as in the TMDL program). Given the complexity of the models and data analysis tools that are ty...

environmental factors are one of the most sensitive issues that should be considered in many decisions. site selection is one of the most widely used spatial decision making problems and is influenced by many environmental factors. the purpose of site selection is to find a set of appropriate spatial alternatives for a particular application. site selection is a multi-criteria decision making p...

The diagnosis of diabetes, based on measured fasting plasma glucose level, depends on choosing a threshold level for which the probability of failing to detect disease (missed diagnosis), as well as the probability of falsely diagnosing disease (false alarm), are both small. The Bayesian risk provides a tool for aggregating and evaluating the risks of missed diagnosis and false alarm. However, ...

The use of intervention for time series modelling is a well established technique for on-line forecasting and decision-making in the context of Bayesian dynamic linear models. Intervention has also been recently used in (non-dynamic) Bayesian networks to investigate causal relationships between variables, and in dynamic Bayesian networks to investigate lagged causal relationships between time s...

Bayesian networks are limited in differentiating between causal and spurious relationships among decision factors. Decision making without differentiating the two relationships cannot be effective. To overcome this limitation of Bayesian networks, this study proposes linking Bayesian networks to structural equation modeling (SEM), which has an advantage in testing causal relationships between f...