Hs305 Research Methods For Health Sciences Unit 6 Discussion

NameInstitutionCourseDate Data miningData mining involves creating information from raw data. It entails discovering anomalies, correlations and patterns within a vast set of data to predict outcomes (Raghupathi, 2016). This paper outlines the pros and cons of data mining, an example of when data mining was used, an outcome that provided an incorrect assumption, and how that can be avoided in the future. Pros of data miningIn the health industry, data mining is used in the identification of best practices and most effective treatments by care providers; it's also used in the developing standards of care and guidelines (Raghupathi, 2016). With this, patients with high-risk diseases able to receive the best, affordable care of their health. Data mining is also used for the improvement of patient satisfaction, increment in operating efficiency, and to decrease cost by Healthcare organizations. Cons of data mining Data mining can lead to inaccurate predictions resulting from inappropriate modelling since it involves extracting information from an already expecting data set. Inaccurate predictions result in wrong decisions (Raghupathi, 2016). Data mining also leads to the reliability of medical data. In today's highly dynamic world, relying on previous data will possibly temper