Decision Tree Analysis and Genetic Algorithm

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Decision Tree Analysis

In data mining, the decision tree analysis is used to determine the best choice from various possible options. Through this process, researchers and managers get an opportunity to evaluate the risks, benefits and inconsistencies associated with the decisions. The first step is structuring the problems or issues being faced by the organization as a tree. At the end of each branch, all the benefits are listed to help in evaluating the path with the most benefits. After the benefits have been determined, the next step involves assigning subjective probabilities to all the activities on the tree (Qu, Adam, Yasui, Ward, & Cazares, 2002). On each of the choices, the possibilities on risks, errors and ambiguities are listed to help in evaluating the best option.

The benefits of making certain decisions are associated with consequences to develop better comparison strategies that would come up with the best decision. This enhances the decision-making process and enables corporations to develop a model for dealing with the company (Qu, Adam, Yasui, Ward, & Cazares, 2002). The company focuses on the strategies that are in place and the efficiency of the strategies selected. The persons engaged in the process find better means of engaging in the process and develop comparisons based on the models present.

Arts could be used to determine the best way to treat an individual suffering from a chronic illness. Through the strategies used, the illnesses can be determined and a solution pertaining to the patients being treated, found. A more cost effective and efficient approach, in which the detained get their correction in a more economical manner, has been found. Here detained persons get a more practical feeling an...

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...o other methods of data collection. Under this strategy, most individuals can be tested and related to certain illnesses, most of which are genetically motivated (Cavill, Keun, Holmes, LIndon, & Nicholson, 2009). This also assists in the development of curative alternatives, in metabolism and other biological processes. Genetics also helps in analysis and formulation of treatment criteria.

Works Cited

Cavill, R., Keun, H. C., Holmes, E., LIndon, J. C., & Nicholson, J. K. (2009). Genetic Algorithms for Simultaneous Variable and Sample Selection in Metabonomics. Bioinformatics, 25 (1), 112-118.

Qu, Y., Adam, B. L., Yasui, Y., Ward, M., & Cazares, L. H. (2002). Boosted Decision Tree Analysis Of Surface-Enhanced Laser Desorption/Ionization Mass Spectral Serum Profiles Discriminates Prostate Cancer From Noncancer Patients. Clinical Chemistry, 48, 1835-1843.

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