Research Article
Predicting the Progression of IgA Nephropathy using Machine Learning Methods
@INPROCEEDINGS{10.4108/icst.bict.2014.257893, author={Junhyug Noh and Dharani Punithan and Hajeong Lee and Jung Pyo Lee and Yon Su Kim and Dong Ki Kim and Robert McKay}, title={Predicting the Progression of IgA Nephropathy using Machine Learning Methods}, proceedings={8th International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)}, publisher={ICST}, proceedings_a={BICT}, year={2015}, month={2}, keywords={immunoglobulin a nephropathy (igan) end-stage renal disease (esrd) classification and regression trees (cart) logistic regression neural networks receiver operating characteristic (roc) area under curve (auc) missing completely at random (mcar)}, doi={10.4108/icst.bict.2014.257893} }- Junhyug Noh
Dharani Punithan
Hajeong Lee
Jung Pyo Lee
Yon Su Kim
Dong Ki Kim
Robert McKay
Year: 2015
Predicting the Progression of IgA Nephropathy using Machine Learning Methods
BICT
ACM
DOI: 10.4108/icst.bict.2014.257893
Abstract
We predict the progression of Immunoglobulin A Nephropathy using three of the most widely used supervised classification machine learning algorithms : Classification and Regression Trees, Logistic Regression (in two different forms), and Feed-Forward Neural Networks. The problem is treated as a classification problem, of predicting progression to end-stage renal disease in the ten years following initial diagnosis. All four methods yielded good classifiers, with AUC performance between 0.85 (decision tree) and 0.89 (neural network). The results were generally in-line with expectations, with poor kidney performance on presentation, and evident macroscopic and microscopic damage, all associated with poorer prognosis. However, the association between normal systolic blood pressure status and poor prognosis, for some patients under specific conditions, was entirely unanticipated, and warrants further investigation.
