A tree-based decision model to support prediction of the severity of asthma exacerbations in children

This paper describes the development of a tree-based decision model to predict the severity of pediatric asthma exacerbations in the emergency department (ED) at 2 h following triage. The model was constructed from retrospective patient data abstracted from the ED charts. The original data was preprocessed to eliminate questionable patient records and to normalize values of age-dependent clinical attributes. The model uses attributes routinely collected in the ED and provides predictions even for incomplete observations. Its performance was verified on independent validating data (split-sample validation) where it demonstrated AUC (area under ROC curve) of 0.83, sensitivity of 84%, specificity of 71% and the Brier score of 0.18. The model is intended to supplement an asthma clinical practice guideline, however, it can be also used as a stand-alone decision tool

Spare parts classification in industrial manufacturing using the dominance-based rough set approach

Classification is one of the critical issues in the operations management of spare parts. The issue of managing spare parts involves multiple criteria to be taken into consideration, and therefore, a number of approaches exists that consider criteria such as criticality, price, demand, lead time, and obsolescence, to name a few. In this paper, we first review proposals to deal with inventory control. We then propose a three-phase multicriteria classification framework for spare parts management using the dominance-based rough set approach (DRSA). In the first phase, a set of ‘if–then’ decision rules is generated from historical data using the DRSA. The generated rules are then validated in the second phase by using both the automated and manual approaches, including cross-validation and feedback assessments by the decision maker. The third and final phase is to classify an unseen set of spare parts in a real setting. The proposed approach has been successfully applied to data collected from a manufacturing company in China. The proposed framework was practically tested on different spare parts and, based on the feedback received from the industry experts, 96% of the spare parts were correctly classified. Furthermore, the cross-validation results show that the proposed approach significantly outperforms other well-known classification methods. The proposed approach has several important characteristics that distinguish it from existing ones: (i) it is a learning-set based analysis approach; (ii) it uses a powerful multicriteria classification method, namely the DRSA; (iii) it validates the generated decision rules with multiple strategies; and (iv) it actively involves the decision maker during all the steps of the decision making process

Effects of the agricultural sector on the physico-chemistry and the ecosystem quality of inland waters

The aim of this research was to identify the links between the agricultural sector and the physicochemical condition and ecosystem health of inland waters. In order to create a comprehensive model, two analytical tools have been used, on the one hand the "Driving forces – Pressure - State – Impact - Response" (DPSIR) modeling framework and, on the other, the decision trees data mining technique with the use of an improved version of the C4.5 algorithm called See5. The datamining applications were performed in three consecutive phases. In the first phase “State” data were tested against “Impact” data in order to identify possible links. In the second phase the same was done with “Pressure” data against “State” data and in the third phase “Driving Forces” data where tested against “Pressure” data. The results of the first phase indicate, as expected, that there is a linkage between several physicochemical characteristics of the lakes examined and the amount of fish caught from those lakes. These characteristics are the electrical conductivity, the Cl- concentration, the pH and the oxygen saturation percentage of the water. When these parameters were examined against the agricultural “Pressures” data in the second phase, the results showed that the agricultural production area and the agricultural production amounts, in general as well as for specific products, were linked to the physicochemical characteristics. The results of the third phase showed that although three non-environmentally friendly financial assistance programmes were found to be directly linked to agricultural production, three environmentally friendly programmes showed no links to the agricultural production which can be attributed to the larger size and geographical scope of the non-environmentally friendly programmes. Apart of the detailed results obtained, it was also found that data mining techniques are able to assess the effects of the agricultural sector on the physicochemical of inland waters and it is expected that they could also perform well with other types of environmental data. Thus, data mining is a powerful tool in the hands of environmental policy maker and should be used wherever the available data permit it