# IgA Nephropathy Prediction in Children with Machine Learning Algorithms
## 5 Discussion  
In the paper, the use of machine learning algorithms for predicting IgAN in children was researched. At first, 16 features (serum creatinine, focal segmental glomerular sclerosis, crescent ratio, albumin, uric acid, glomerulosclerosis, cholesterol, urine NAG enzyme, eGFR, triglycerides, E, T, C, M, IgM, and C3) were chosen as the input of the classifiers by the chi-square test for dimension reduction. Subsequently, the XGBoost, RF, CatBoost, KNN, SVM, and ELM models were applied to predict IgAN in children. Finally, a decision-making system was build based on the best performing model and Django framework. The results that are shown in Table 5 and Figure 3 indicate that the XGBoost model can provide better performance when compared to other models for the medical application that was considered in this study. The AUC value, accuracy, precision, recall, and f1-score of XGBoost were 85.11%, 78.60%, 75.96%, 76.70%, and 76.33%, respectively. While the AUC value, accuracy, precision, recall and f1-score of RF (76.42%, 74.26%, 72.82%, 73.53%, 85.07%), CatBoost (76.42%, 73.79%, 73.79%, 73.79%, 73.79%, 73.79%, 73.79%, 73.79%, 73.79%, 73.79%, 74.54%), KNN (75.55%, 73.27%, 71.84%, 72.55%, 80.90%), SVM (76.42%, 73.33%, 74.76%, 74.04%, 82.72%), and ELM (75.98%, 72.64%, 74.76%, 73.68%, 81.74%) models are all lower than those of XGBoost. Here, we can highlight an advantage of XGBoost, because we not only need interpretable models to assist clinical decision-making, but also help clinicians to discover hidden factors that affect the disease. The XGBoost algorithm has a regularization term to prevent overfitting. Moreover, XGBoost can specify the default direction of the branch for missing values or specified values, which can greatly improve the efficiency of the algorithm. More importantly, the XGBoost model has high generalization performance and it can clearly output the important scores of each attribute, namely it is interpretable, which is required by clinical medicine.  
Despite the potential of this research, there are several limitations. First, the collected dataset is relatively small, and it cannot fully cover kidney cases in children, which leads to inaccurate predictions of special kidney disease cases in children. Therefore, the dataset needs to be increased and feature processing needs to be more refined by applying data mining techniques for predicting IgAN in children. Second, the prediction system is suitable for children with IgAN in the age range of 0-18 years old, but not for adults with IgAN.  
In the future, we will devote to improve the accuracy of the model, perfect the system, add database to the system, expand the training dataset, and complement the system with an error correction function.  
**Author Contributions:** Conceptualization, P.Z. and N.S.; methodology, R.W. and N.S.; software, R.W. and N.S.; validation, R.W. and N.S.; formal analysis, P.Z. and R.W.; investigation, P.Z. and N.S.; resources, N.S.; data curation, R.W.; writing--original draft preparation, R.W.; writing--review and editing, P.Z. and R.W. All authors have read and agreed to the published version of the manuscript.  
Figure 3: Receiver operating characteristic (ROC) curve of models.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 11401172, Key Scientific Research Project Plan of Colleges and Universities in Henan Province, grant number 20A520012, and the Pediatric Medical Innovation Team of Jiangsu Province, grant number CXTDA2017022.  
**Conflicts of Interest:** The authors declare no conflict of interest.