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Although SOA was successful for many years, today it has reached its limits due to many reasons including the heavy stack of standards that make difficult the creation of applications instead of simplifying it. Another major drawback is the neglect of the creative potential of the user, not involved in the life cycle of the SOA software. In this paper, we present a solution to enhance SOA, by enjoying the benefits of Web 2.0 technologies, and more particularly of mashups. First, we are interested in describing the benefits of the Mashup used with SOA, based on several case studies; then, we present the new approach for a user-centric SOA, becoming possible through a Mashup stack that contains the technologies aiming at enhancing SOA and making it user-centric for more added value to enterprises. in 1991. His research interests include cooperation of distributed systems, ontologies and semantic web and e-learning. This paper is a revised and expanded version of a paper entitled 'Towards an approach for a user centric SOA' presented at The Third

Healthcare studies prove that heart disease has increased in recent decades and the growth of patients suffering from heart problems does not stop. In this regard, various data mining techniques have been used by machine learning researchers to support health professionals in the decision-making of this disease. Many of these techniques are based on basic machine learning classifiers, others integrate these classifiers in streaming systems in order to accelerate the execution time. However, some heart situations demand early detection to reduce the chance of having a dangerous illness and the existing machine learning solutions are not appropriate for real-time analysis, because we need to accelerate the algorithms themselves. In this paper, an online algorithm called Enhanced Hoeffding Anytime Tree (EHATT) is proposed to efficiently predict heart disease. EHATT is based on Hoeffding Anytime Tree (HATT), the last version of incremental decision trees. The amelioration that was made by EHATT on HATT, is the change of the node splitting evaluation function with another more suitable for split measures. To examine the performance of EHATT, four metrics are evaluated: classification accuracy, time, memory, and tree size. The experiment results show that EHATT achieves good performance to predict heart disease.

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