IgA Nephropathy Prediction in Children with Machine Learning Algorithms

Future Internet

Nianfeng Shi¹  Rongqin Wang²  Ping Zhang³ 

[1] School of Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang 471000, China;School of Information Engineering, Zhengzhou University, Zhengzhou 450000, China;School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471000, China;

关键词: IgA nephropathy;    machine learning;    XGBoost;    prediction;    decision-making system;   

DOI  :  10.3390/fi12120230

来源: DOAJ

【 摘 要 】

Immunoglobulin A nephropathy (IgAN) is the most common primary glomerular disease all over the world and it is a major cause of renal failure. IgAN prediction in children with machine learning algorithms has been rarely studied. We retrospectively analyzed the electronic medical records from the Nanjing Eastern War Zone Hospital, chose eXtreme Gradient Boosting (XGBoost), random forest (RF), CatBoost, support vector machines (SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM) models in order to predict the probability that the patient would not reach or reach end-stage renal disease (ESRD) within five years, used the chi-square test to select the most relevant 16 features as the input of the model, and designed a decision-making system (DMS) of IgAN prediction in children that is based on XGBoost and Django framework. The receiver operating characteristic (ROC) curve was used in order to evaluate the performance of the models and XGBoost had the best performance by comparison. The AUC value, accuracy, precision, recall, and f1-score of XGBoost were 85.11%, 78.60%, 75.96%, 76.70%, and 76.33%, respectively. The XGBoost model is useful for physicians and pediatric patients in providing predictions regarding IgAN. As an advantage, a DMS can be designed based on the XGBoost model to assist a physician to effectively treat IgAN in children for preventing deterioration.

【 授权许可】

Unknown