Data mining may enable healthcare organizations, with analysis of the different prospects and connection between seemingly unrelated information, to anticipate trends in the patient's medical condition and behavior. Raw data are large and heterogeneous from healthcare organizations. It needs to be collected and arranged, and its integration enables medical information systems to be integrated in a united way. Health data mining offers unlimited possibilities to evaluate numerous less obvious or secret data models utilizing common techniques for study. Association rule mining (ARM) is an effective technique for detecting the connection of the data which are the most commonly used and influential algorithms in ARM for an Apriori algorithm. However, it generates a large amount of rules and does not guarantee the efficiency and value of the knowledge created. In order to overcome this issue, an enhanced Apriori algorithm (EAA) based on the knowledge of a context ontology (EAA-SMO) methodology for sequential minimal optimization (SMO) is suggested. The simple knowledge is to establish the ideas of ontology as a hierarchical structure of the conceptual clusters of specific subjects, which comprises ''similar'' concepts that mean an exact category of the knowledge within the domain. There is an interesting rule for each cluster based on the correlation between the items. In addition, the rule developed is classified as a prediction model for anomaly detection based on SMO regression. The experimental analysis demonstrates the proposed method improved 2% of accuracy and minimizes the execution time by 25% when compared to semantic ontology.

The development for data mining technology in healthcare is growing today as knowledge and data mining are a must for the medical sector. Healthcare organizations generate and gather large quantities of daily information. Use of IT allows for the automation of data mining and information that help to provide some interesting patterns which remove manual tasks and simple data extraction from electronic records, a process of electronic data transfer which secures medical records, saves lives and cuts the cost of medical care and enables early detection of infectious diseases. In this research paper an improved Apriori algorithm names Enhanced Parallel and Distributed Apriori (EPDA) is presented for the health care industry, based on the scalable environment known as Hadoop MapReduce. The main aim of the work proposed is to reduce the huge demands for resources and to reduce overhead communication when frequent data are extracted, through split-frequent data generated locally and the early removal of unusual data. The paper shows test results, whereby the EPDA performs in terms of the time and number of rules generated with a database of healthcare and different minimum support values.

The Editor-in-Chief and the publisher have retracted this article. This article was submitted to be part of a guestedited issue. An investigation concluded that the editorial process of this guest-edited issue was compromised by a third party and that the peer review process has been manipulated. Based on the investigation's findings, the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article.M. Sornalakshmi and S. Balamurali disagree with this retraction. Seifedine Kadry and Bala Anand Muthu have not explicitly stated whether they agree to this retraction.

Ensuring that the IT/business functions of an organization realize its business objectives has long been recognized as a critically important question. This paper reports on a project that seeks to overturn established management orthodoxy by establishing that business objectives can be adequately modeled by leveraging a domain ontology and that methodological and tool support can be provided for the task of correlating the objectives of an organization and its service offerings. This paper presents an interim report from this project that describes how to leverage a domain ontology in i) building business objective/goal models in a top-down manner (required to be able to refine these to a level where there could be an ontological match between the languages used to describe objectives and services); ii) assessing the degree of ontological match between low-level objectives and business services as a step towards an automated framework for establishing strategic service alignment. We provide a brief illustration of our in-progress implementation within a toolkit called ServAlign.

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