# IgA Nephropathy Prediction in Children with Machine Learning Algorithms
## 2 Materials and Methods  
### Dataset  
The initial data include electronic medical records of 1167 patients aged 0-18 years old from 2003 to 2019, which came from the electronic medical records of the Nanjing Eastern War Zone Hospital. After the initial data cleaning and deletion of information with missing values, the dataset contained 1146 records. Each record contained 37 attributes, where the first 36 attributes are independent variables that correspond to the patient information, while the remaining attribute is the dependent variable of clinical interest. The 36 independent variables were collected according to five aspects, which are epidemiology, blood test, urine test, renal pathology, and treatment options. In detail, age, sex and hypertension are classified as epidemiology. Serum creatinine (Scr), cholesterol (CHOL), triglycerides (TG), albumin (ALB) complement C3, and glomerular filtration rate (eGFR) are blood test indicators. Urine tests include urine C3 (Ur_C3), \(\alpha\)2-m, urine NAG enzyme (Ur_NAG), urine RBP (Ur_RBP), and uric acid (UA). ACEL_ARB, immunosuppression therapy, lipid lowering, and tonsillectomy are the treatment options. The remaining 19 attributes M, E, S, T, C, IgA, IgG, IgM, C3, C4, C1q, loop necrosis, focal segmental glomerular sclerosis (FSCS), glomerulosclerosis, arterial hyaline degeneration, crescent ratio, medullary interstitial fibrosis, thickening and stratification of elastic layer of interlobular artery, and vacuolar degeneration of arteriole smooth muscle cells are included in renal pathology. The dependent variable was whether the patient would reach the end-stage renal disease within five years, which was expressed by ESRD. These independent variable attributes are divided into continuous independent variables and categorical independent variables according to whether they are continuous values or not, as shown in Tables 1 and 2. Specifically, Table 1 shows the range, mean, and standard deviation for continuous independent variables and Table 2 displays the possible values, numeric value, as well as the number of records for categorical independent variables. Table 3 displays the possible values, numeric values, as well as the number of records for categorical dependent variables. The ESRD is distributed in 567 and 578 records, which correspond to the categories yes and no, respectively, which indicates that patients reach the final stage of IgAN or do not reach that within five years, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Range** & **Mean** & **Standard Deviation** \\ \hline Age (years) & 0–18 & 14 & 3.59 \\ Scr (\(\mu\)mol/L) & 16.00–1154.50 & 71.12 & 67.86 \\ CHOL (mmol/L) & 0.20–19.00 & 5.49 & 2.70 \\ TG (mmol/L) & 0.17–22.00 & 1.62 & 1.25 \\ UA (\(\mu\)mol/L) & 0.53–777.00 & 347.87 & 97.63 \\ ALB (g/mL) & 7.60–65.20 & 41.88 & 120.48 \\ Complement C3 (g/L) & 0.24–25.00 & 1.06 & 0.74 \\ FSGS & 0.00–61.50 & 4.20 & 8.75 \\ Spherically sclerotic & 0.00–84.20 & 3.75 & 9.21 \\ Crescent ratio & 0.00–77.80 & 5.25 & 9.50 \\ eGFR (mL-min\({}^{-1}\)-(1.72 m\({}^{2}\))\({}^{-1}\)) & 4.90–141.75 & 111.77 & 21.11 \\ \hline \hline \end{tabular}  
* Scr-Serum creatinine; CHOL-cholesterol; TG-triglycerides; UA-uric acid; ALB-albumin; FSGS-focal segmental glomerular sclerosis; eGFR-glomerular filtration rate.  
\end{table} 
Table 1: Continuous independent variables.