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HCMG101 Health Care System Question: Analyze the case study on Data Mining and Decision Support Systems (DSS), Intelligent decision support in healthcare. Answer: Data mining refers to the technique of identifying patterns in huge data sets concerned with approaches at the intersection of machine learning, database systems as well as statistics. In other terms, data mining refers to the step of analysis of the knowledge realization in database procedures (Yoo et al., 2012). Data mining shares a big potential in the industry of healthcare in order to make health systems to automatically and systematically utilize data as well as analytics to establish inefficiencies as well as the best exercises that advance the care and minimize the costs (Jothi, and Husain, 2015). Some of the specialists of the healthcare believe that chances to advance care as well as minimize costs concurrently might apply to as much as over 25% of total medical care spending. There are several approaches that are needed to be implemented in order to advance the healthcare services which include the following systems: The analytics system – this refers to the approach that incorporates technology as well as specialists to collect information, make sense of it as well as standardize units. The best exercise system – this is concerned with standardizing understanding task systematically deploying evidence-based best exercise to care provision. Researchers define important to study every year on medical best exercises. The adoption system – this is concerned with emphasizing change administration through fresh firm structures. Particularly, it is concerned with executing team which might enable order, enterprise broad adaption of the better exercises. Decision support system refers to a computer software program analyzes a business information and shows it in order clients can make medical decisions with ease. Decision support is an essential maker of the decision in various industries including healthcare. Generally, decision support systems aid makers of the decision to collect and interpret data as well as building a basis for making decisions (Liu et al., 2016). In medical care, medical decision support might play an important responsibility in which mostly they are based on medical guidance as well as evidence-based policies gotten from health science. Although, intelligent decision support systems permit healthcare staffs to rapidly collect data and process it in several ways so as help in diagnosis making as well as treatment decision. The intelligent decision support system is deployed in healthcare in different fields like an evaluation of real-time information from various controlling gadgets, analysis of victims as well as the history of patients for the reason of diagnosis and emerging issues in healthcare databases among others. Intelligent decision support systems it is supported by an architecture that aids in making of decisions by the display of intelligent characteristics might incorporate learning as well as reasoning (Yao, and Azam, 2015). The intelligent decision support systems are needed in the medical care in controlling knowledge in order to help in medical decision making which is then converted into operational intelligence which is interpreted by various operational work areas within the firm. Intelligence decision support systems might assist in various ways in medical decision making at both personal victim level as well as the mass level. References Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K., Gopidi, R., Chang, J. F., & Hua, L. (2012). Data mining in healthcare and biomedicine: a survey of the literature. Journal of medical systems, 36(4), 2431-2448. Jothi, N., & Husain, W. (2015). Data mining in healthcare–a review. Procedia Computer Science, 72, 306-313. Liu, X., Lu, R., Ma, J., Chen, L., & Qin, B. (2016). Privacy-preserving patient-centric clinical decision support system on naive Bayesian classification. IEEE journal of biomedical and health informatics, 20(2), 655-668. Yao, J., & Azam, N. (2015). Web-based medical decision support systems for three-way medical decision making with game-theoretic rough sets. IEEE Transactions on Fuzzy Systems, 23(1), 3-15.

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