New technologies like echocardiography, color Doppler, CT, and MRI provide more direct and accurate evidence of heart disease than heart auscultation. However, these modalities are costly, large in size and operationally complex and therefore are not suitable for use in rural areas, in homecare and generally in primary healthcare set-ups. Furthermore the majority of internal medicine and cardiology training programs underestimate the value of cardiac auscultation and junior clinicians are not adequately trained in this field. Therefore efficient decision support systems would be very useful for supporting clinicians to make better heart sound diagnosis. In this study a rule-based method, based on decision trees, has been developed for differential diagnosis between "clear" Aortic Stenosis (AS) and "clear" Mitral Regurgitation (MR) using heart sounds. For the purposes of our experiment we used a collection of 84 heart sound signals including 41 heart sound signals with "clear" AS systolic murmur and 43 with "clear" MR systolic murmur. Signals were initially preprocessed to detect 1st and 2nd heart sounds. Next a total of 100 features were determined for every heart sound signal and relevance to the differentiation between AS and MR was estimated. The performance of fully expanded decision tree classifiers and Pruned decision tree classifiers were studied based on various training and test datasets. Similarly, pruned decision tree classifiers were used to examine their differentiation capabilities. In order to build a generalized decision support system for heart sound diagnosis, we have divided the problem into sub problems, dealing with either one morphological characteristic of the heart-sound waveform or with difficult to distinguish cases. Relevance analysis on the different heart sound features demonstrated that the most relevant features are the frequency features and the morphological features that describe S1, S2 and the systolic murmur. The results are compatible with the physical understanding of the problem since AS and MR systolic murmurs have different frequency contents and different waveform shapes. On the contrary, in the diastolic phase there is no murmur in both diseases which results in the fact that the diastolic phase signals cannot contribute to the differentiation between AS and MR. We used a fully expanded decision tree classifier with a training set of 34 records and a test set of 50 records which resulted in a classification accuracy (total corrects/total tested) of 90% (45 correct/50 total records). Furthermore, the method proved to correctly classify both AS and MR cases since the partial AS and MR accuracies were 91.6% and 88.5% respectively. Similar accuracy was achieved using decision trees with a fraction of the 100 features (the most relevant). Pruned Differentiation decision trees did not significantly change the classification accuracy of the decision trees both in terms of partial classification and overall classification as well. Present work has indicated that decision tree algorithms decision tree algorithms can be successfully used as a basis for a decision support system to assist young and inexperienced clinicians to make better heart sound diagnosis. Furthermore, Relevance Analysis can be used to determine a small critical subset, from the initial set of features, which contains most of the information required for the differentiation. Decision tree structures, if properly trained can increase their classification accuracy in new test data sets. The classification accuracy and the generalization capabilities of the Fully Expanded decision tree structures and the Pruned decision tree structures have not significant difference for this examined sub-problem. However, the generalization capabilities of the decision tree based methods were found to be satisfactory. Decision tree structures were tested on various training and test data set and the classification accuracy was found to be consistently high.

This article investigates the main barriers to the adoption of B2B e-marketplaces by large enterprises and at the same time the expected benefits that push in the opposite direction towards the adoption, through a case study conducted at the Hellenic Aerospace Industry (HAI), one of the largest industrial enterprises of Greece. The evidence collected from this case has been analyzed using various frameworks from the literature and also the innovation diffusion theory of Rogers, and shed light on the specificity of the large enterprises concerning e-marketplaces adoption.

This article aims at analyzing (i) the factors determining the firms’ propensity to outsource various processes (production of intermediate products, production of final products, R&D activities, ICT activities), and (ii) the impact of outsourcing on firms’ innovation performance, as well as labor productivity. The integral investigation of the determining factors, as well as the impact of outsourcing on innovation and productivity based on the same data in a comparative setting (Switzerland versus Greece) are important new elements and contributions of this study to the existing empirical literature. It has been concluded that in both countries more innovative firms (R&D) were stronger inclined to outsourcing activities than less innovative ones, whereas on the contrary, the educational level of employees and the labor cost showed no effect on outsourcing in both countries. Also, the intensity of ICT use and organizational aspects, especially those related to the formal structure of workplace organization, were relevant for the Swiss firms but not for the Greek firms. With respect to the impact of outsourcing on performance, it has been concluded that it tends to enhance innovation, particularly process innovation, but only weakly productivity (at least directly).