Abstract
This study compares the performance of Logistic Regression and Classification and Regression Tree model implementations in predicting chronic kidney disease outcomes from predictor variables, given insufficient training data. Imputation of missing data was performed using a technique based on k-nearest neighbours. The dataset was arbitrarily split into 10 % training set and 90 % test set to simulate a dearth of training data. Accuracy was mainly considered for the quantitative performance assessment together with ROC curves, area under the ROC curve values and confusion matrix pairs. Validation of the results was done using a shuffled 5-fold cross-validation procedure. Logistic regression produced an average accuracy of about 99 % compared to about 97 % the decision tree produced.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.