@article{(Open Science Index):https://publications.waset.org/pdf/248, title = {Decision Trees for Predicting Risk of Mortality using Routinely Collected Data}, author = {Tessy Badriyah and Jim S. Briggs and Dave R. Prytherch}, country = {}, institution = {}, abstract = {It is well known that Logistic Regression is the gold standard method for predicting clinical outcome, especially predicting risk of mortality. In this paper, the Decision Tree method has been proposed to solve specific problems that commonly use Logistic Regression as a solution. The Biochemistry and Haematology Outcome Model (BHOM) dataset obtained from Portsmouth NHS Hospital from 1 January to 31 December 2001 was divided into four subsets. One subset of training data was used to generate a model, and the model obtained was then applied to three testing datasets. The performance of each model from both methods was then compared using calibration (the χ2 test or chi-test) and discrimination (area under ROC curve or c-index). The experiment presented that both methods have reasonable results in the case of the c-index. However, in some cases the calibration value (χ2) obtained quite a high result. After conducting experiments and investigating the advantages and disadvantages of each method, we can conclude that Decision Trees can be seen as a worthy alternative to Logistic Regression in the area of Data Mining.}, journal = {International Journal of Computer and Information Engineering}, volume = {6}, number = {2}, year = {2012}, pages = {227 - 230}, ee = {https://publications.waset.org/pdf/248}, url = {https://publications.waset.org/vol/62}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 62, 2012}, }