# IgA Nephropathy Prediction in Children with Machine Learning Algorithms
## 1 Introduction  
Immunoglobulin A nephropathy (IgAN) is the most common primary glomerular disease all over the world and it is a major cause of end-stage renal disease (ESRD). Additionally, there are similarities and differences of IgAN in adults and children. Clinical studies showed that the survival rate of IgAN patients over 18 years old was 70-80% within 10 years. Among them, 20-30% patients will progress to ESRD and need renal replacement therapy [1]. [PERSON] et al. [2] found that the five-year survival rate of children with IgAN was 94-98%, and the 20-year survival rate was 70-89%. Although IgAN in children has relatively lower incidence and less progress in childhood as compared to adults, there is a continuous hazard of progression as children grow older [3]. The prognosis of children with IgAN varies from remission to progression to ESRD. Early detection and effective intervention are important in improving the outcome of IgAN, and the renal biopsy is still the cornerstone of the correct diagnosis of IgAN. However, most patients have entered ESRD at the time of diagnosis, since it is difficult for patients to accept invasive renal biopsy. It is difficult to determine a fixed treatment plan for IgAN in children because it is more troublesome to gather up the course of IgAN in children than in adults. Therefore, it is necessary to develop new predictive models for the progression of IgAN in children inorder to guide the selection of cases to be treated [4]. Machine learning has been successfully applied in many fields, providing a shield for diagnosis of IgAN for non-invasive diagnose in asymptomatic cases. Traditional medical treatment methods rely entirely on doctors' diagnosis and the treatment of patients. In this way, it is difficult to distinguish between diseases with similar symptoms and discover the hidden diseases, leading to misdiagnosis, which may delay the patient's treatment or endanger the patient's life. With the explosive growth of electronic medical records, machine learning in medicine has attracted the attention of many scholars [5; 6; 7]. [PERSON] et al. proposed intelligent smart healthcare monitoring systems that are based on machine learning approaches to extract useful features from the collected healthcare data of patients, reduce the dimensionality of the data, and improve the classification precision [8; 9]. ESRD has the characteristics of high disability, high mortality, and high medical expenses. A lot of researches were invested in the early detection and prediction of kidney disease by machine learning algorithms in order to avoid the occurrence of ESRD, such as the prediction of the kidney disease [10; 11], the formulation of the best course of treatment [12], the evaluation of the risk stratification [13], and so on. The goal of treating IgAN is still to delay the progression of IgAN, since the exact treatment plan for IgAN has not yet been established. Researchers mainly focus on the prognosis [14; 15; 16], risk stratification [17; 18; 19; 20], and ESRD prediction of IgAN [21; 22], whose patients are over 18 years old. The researches on IgAN in children focused on pathological characteristics and statistics [23; 24; 25], but there are few on the prediction of IgAN in children based on machine learning. Machine learning algorithms are an effective method for solving the high-dimensional problem of medical data, and it plays an important role in smart medical care. In this paper, a novel method is proposed for predicting the probability of children patients with IgAN reaching ESRD in five years. The main contributions of this paper are:  
* A dataset about IgAN in children was created. And the chi-square test was used to extract the most useful features from the dataset. * EXtreme Gradient Boosting (XGBoost) was adopted in order to predict whether IgAN disease in children patients would reach ESRD or not within five years using a new dataset instead of the traditional clinical pathology.