Han Xin, Zheng Xiaonan, Wang Ying, Sun Xiaoru, Xiao Yi, Tang Yi, Qin Wei
Department of Nephrology, Institute of Urology, West China Hospital, Sichuan University, Chengdu 610041, China.
Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu 610041, China.
Ann Transl Med. 2019 Jun;7(11):234. doi: 10.21037/atm.2018.12.11.
IgA nephropathy (IgAN) is the most common glomerulonephritis worldwide and up to 40% will develop end-stage renal disease (ESRD) within 20 years. However, predicting which patients will progress to ESRD is difficult. The purpose of this study was to develop a predictive model which could accurately predict whether IgAN patients would progress to ESRD.
Six machine learning algorithms were used to predict whether IgAN patients would progress to ESRD: logistic regression, random forest, support vector machine (SVM), decision tree, artificial neural network (ANN), k nearest neighbors (KNN). Nineteen demographic, clinical, pathologic and treatment parameters were used as input for the prediction models.
Random forest is best able to predict progression to ESRD. The model had accuracy of 93.97% and sensitivity and specificity of 80.60% and 95.27%, respectively.
Machine learning algorithms can effectively predict which patients with IgA nephropathy will progress to end stage renal disease.
IgA 肾病（IgAN）是全球最常见的肾小球肾炎，高达 40%的患者会在 20 年内发展为终末期肾病（ESRD）。然而，预测哪些患者会进展为 ESRD 很困难。本研究的目的是开发一种预测模型，能够准确预测 IgAN 患者是否会进展为 ESRD。
使用六种机器学习算法预测 IgAN 患者是否会进展为 ESRD：逻辑回归、随机森林、支持向量机（SVM）、决策树、人工神经网络（ANN）、k 近邻（KNN）。19 个人口统计学、临床、病理和治疗参数用作预测模型的输入。
随机森林最能预测进展为 ESRD。该模型的准确率为 93.97%，敏感性和特异性分别为 80.60%和 95.27%。
机器学习算法可以有效预测哪些 IgA 肾病患者会进展为终末期肾病。
