Machine learning model based on clinical,ultrasonic features and radiomics for predicting renal function damage degree in patients with chronic kidney disease
Objective To observe the value of machine learning(ML)models based on clinical,ultrasonic features and radiomics for predicting renal function damage degree in patients with chronic kidney disease(CKD).Methods Data of 199 CKD patients were retrospectively analyzed.The patients were randomly divided into training set(n=179)and validation set(n=20)at the ratio of 9∶1,and further classified as mild-moderate or severe renal function damage according to estimated glomerular filtration rate(eGFR).Multivariate logistic analysis was used to analyze clinical and ultrasonic features,so as to screen the independent predictors of renal function damage degree of CKD patients.Then clinical-ultrasonic model,radiomics model and combined model were constructed using support vector machine(SVM),extreme gradient boosting(XGBoost)and logistic regression(LR),respectively.Receiver operating characteristic(ROC)curves were drawn,the area under the curves(AUC)were calculated to evaluate the efficacy of each model for predicting renal function damage degree of CKD patients.Results Renal length was an independent predictive factors for renal function damage degree of CKD patients(P＜0.05).Among models obtained with different algorithms,modelc1inical-ultrasound,modelradiomics and modelcombination obtained with SVM had the highest prediction efficacy,in training set,the sensitivity,specificity,accuracy and AUC of SVM modelclinical-ultrasound was 81.93％,62.50％,71.51％and 0.722,of SVM modelradiomics was 89.16％,70.83％,79.33％and 0.800,of SVM modelcombination was 84.34％,80.21％,82.12％and 0.822,respectively,in validation set,the sensitivity,specificity,accuracy and AUC of SVM modelclinical-ultrasound was 75.00％,66.67％,70.00％and 0.708,of SVM modelradiomics was 75.00％,58.33％,65.00％and 0.667,of SVM modelcombination was 87.50％,75.00％,80.00％and 0.812,respectively.Conclusion ML models based on ultrasonic features and radiomics could be used to predict renal function damage degree in patients with CKD,and SVM modelcombination had the best efficacy.
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