Machine Learning in Prediction of IgA Nephropathy Outcome: A Comparative Approach

We are overwhelmed by a deluge of data and, although its interpretation is challenging, fortunately, information technology comes to the rescue. One of the tools is artificial intelligence, allowing the identification of relationships between variables and their arbitrary classification. We focused on the assessment of both the remission of proteinuria and the deterioration of kidney function in patients with IgA nephropathy, comparing several methods of machine learning. It is of utmost importance to respond to subtle changes in kidney function, which will lead to a deceleration of the disease. This goal has been achieved by analyzing regression techniques, predicting the difference in serum creatinine concentration. We obtained the performance of the tested models which classified patients with high accuracy (Random Forest Classifier showed an accuracy of 0.8-1.0, Multi-Layer Perceptron an Area Under Curve of 0.8842-0.9035 and an accuracy of 0.7527-1.0) and regressors with a low estimation error (Decision Tree Regressor showed MAE 0.2059, RMSE 0.2645). We have demonstrated the impact of both model selection and input features on performance. Application of machine learning methods requires careful selection of models and assessed parameters. The computing power of modern computers allows searching for the models most effective in terms of accuracy.

Automated summary

This study focused on evaluating proteinuria remission and kidney function deterioration in patients with IgA nephropathy using various machine learning methods, demonstrating the significant impact of model selection and input features on performance. The tested models accurately classified patients and had low estimation error in regression, showing the importance of careful selection of models and parameters for machine learning applications.