Romana Koprivec (2012) Prediction of a kidney disease using machine learning methods. EngD thesis.
A group of researchers announced a competition for making diagnostic models that predict intensity of obstructive nephropathy. In this thesis we have developed a regression model which can predict the level of the illness according to the molecular profile. This model is intended to predict two target values: pelvic diameter and differential renal function. We wanted to improve the prediction models with the knowledge about potential connections between biological levels. We have implemented a procedure with the help of the published data about connections between attributes and Mann-Whitney test, which connects two different databases on two different groups of samples. The lowest relative root mean squared error (RRMSE) has been achieved using locally weighted regression. RRMSE of the method has been lower on connected databases than on the original databases. The result on test dataset has improved as well. The best estimated regression methods on train and test dataset have differentiated because of extraordinary small number of samples. Some models had higher RRMSE on train dataset; however, they achieved better results on test dataset. Nevertheless, with the best model according to RRMSE on train dataset we have exceeded the best-published result on the competition for 81%.
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