Xin Han1, Xiaonan Zheng2, Ying Wang3, Xiaoru Sun4, Yi Xiao4, Yi Tang1, Wei Qin1. 1. Department of Nephrology, Institute of Urology, West China Hospital, Sichuan University, Chengdu 610041, China. 2. Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu 610041, China. 3. Department of Mathematics, University of Oklahoma, Norman, OK, USA. 4. West China school of Medicine, Sichuan University, Chengdu 610041, China.
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: Machine learning algorithms can effectively predict which patients with IgA nephropathy will progress to end stage renal disease.
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: Machine learning algorithms can effectively predict which patients with IgA nephropathy will progress to end stage renal disease.
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