Predicting the Progression of IgA Nephropathy using Machine Learning Methods

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8th International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)
Junhyug Noh1, Dharani Punithan2, Hajeong Lee3, Jung Pyo Lee4, Yon Su Kim3, Dong Ki Kim3, Robert McKay1
1: Computer Science and Engineering, College of Engineering, Seoul National University
2: Institute of Computer Technology, Seoul National University
3: Internal Medicine, College of Medicine, Seoul National University
4: Department of Internal Medicine, Seoul National University Boramae Medical Center

    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.