Decision Tree Analysis In data mining, decision tree analysis is used to determine the best choice among various possible options. Through this process, researchers and managers have the opportunity to evaluate the risks, benefits, and inconsistencies associated with decisions. The first step is to structure the problems or issues faced by the organization as a tree. At the end of each branch all the benefits are listed to help you evaluate the path with the most benefits. After the benefits have been determined, the next step involves assigning subjective probabilities to all activities in the tree (Qu, Adam, Yasui, Ward, & Cazares, 2002). The possibilities for risks, errors and ambiguities are listed on each choice to help you evaluate the best option. The benefits of making certain decisions are associated with consequences to develop better comparison strategies that would lead to the best decision. This improves decision making and allows companies to develop a model for dealing with the company (Qu, Adam, Yasui, Ward, & Cazares, 2002). The company focuses on the strategies implemented and the efficiency of the selected strategies. People involved in the process find better means to engage in the process and develop comparisons based on the models present. The arts could be used to determine how best to treat an individual with a chronic illness. Through the strategies used it is possible to determine the diseases and find a solution relevant to the patients to be treated. A more effective and cost-effective approach has been found, where inmates get their fix more cheaply. Here detained people gain a more practical feel and...... middle of paper ......or other data collection methods. With this strategy, most individuals can be tested and linked to certain diseases, most of which are genetically motivated (Cavill, Keun, Holmes, LIndon, & Nicholson, 2009). This also helps in the development of healing alternatives, metabolism and other biological processes. Genetics also helps in the 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 variables and sample selection in metabonomics. Bioinformatics, 25(1), 112-118.Qu, Y., Adam, B.L., Yasui, Y., Ward, M., & Cazares, L.H. (2002). Enhanced decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from non-cancer patients. Clinical Chemistry, 48, 1835-1843.