Machine learning prediction of stone-free rate in patients with ureter stone after treatment of extracorporeal shock wave lithotripsy

doi:10.3877/cma.j.issn.1674-3253.2021.04.003

Abstract

Abstract:

Objective

To compare the performance of machine learning prediction model for success rate of extracorporeal shock wave lithotripsy (ESWL) treating ureter stone.

Methods

1 116 patients who underwent ESWL for ureter stone were enrolled. Clinical data including patient’s gender, age, height, weight, disease course, clinical symptom and characteristics of stone and the outcome of treatment were collected. Prediction model was established by random forest, extreme gradient boosting trees and logistic regression in Python 3.7.

Results

Overall predictive accuracy and area under curve (AUC) calculated by Logistic regression prediction model were 84.67% and 0.70. Results of random forest and extreme gradient boosting trees prediction model were 91.76%, 0.9904 and 98.75%, 0.9998. The three prediction models revealed stone-free rate impacted by patients’ stone site and stone burden.

Conclusions

Machine learning models are better than logistic regression for predicting success rate of ESWL treating ureter stone.

Key words: Extracorporeal shock wave lithotripsy, Ureter stone, Machine learning, Random forest, Extreme gradient boosting trees

Qi Hou, Yang Xiang, Nashan Wu, Yue Xiao, Long Xiao, Xiao Li, Rui Wang, Zhongyi Sun. Machine learning prediction of stone-free rate in patients with ureter stone after treatment of extracorporeal shock wave lithotripsy[J]. Chinese Journal of Endourology(Electronic Edition), 2021, 15(04): 280-284.

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References 15

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变量		例数	碎石成功率(%)
性别
	男	799	85.4
	女	317	81.7
患侧
	左	609	86.4
	右	507	82.2
结石部位
	上段	613	80.9
	中段	121	76.9
	下段	382	92.1
尿路刺激征
	有	380	86.3
	无	736	83.3
肾绞痛
	有	666	87.8
	无	450	79.1

变量		例数	碎石成功率(%)
性别
	男	799	85.4
	女	317	81.7
患侧
	左	609	86.4
	右	507	82.2
结石部位
	上段	613	80.9
	中段	121	76.9
	下段	382	92.1
尿路刺激征
	有	380	86.3
	无	736	83.3
肾绞痛
	有	666	87.8
	无	450	79.1

算法模型	测试模式1 （总体100%验证集和测试集）				测试模式2 （总体90%验证集和总体10%测试集）				测试模式3 （总体80%验证集和总体20%测试集）
算法模型	准确率	敏感度	特异度	AUC	准确率	敏感度	特异度	AUC	准确率	敏感度	特异度	AUC
随机森林模型	91.76%	1	0.4743	0.9904	84.95%	0.9851	0.1190	0.7624	84.5%	0.9819	0.1086	0.7448
极致梯度提升树模型	98.75%	1	0.92	0.9998	83.25%	0.9458	0.2216	0.7232	83.33%	0.9490	0.2114	0.7242
Logistic回归模型	84.77%	0.9936	0.0629	0.7282	84.59%	0.9926	0.0562	0.7257	84.77%	0.9947	0.0571	0.7183

算法模型	测试模式1 （总体100%验证集和测试集）				测试模式2 （总体90%验证集和总体10%测试集）				测试模式3 （总体80%验证集和总体20%测试集）
算法模型	准确率	敏感度	特异度	AUC	准确率	敏感度	特异度	AUC	准确率	敏感度	特异度	AUC
随机森林模型	91.76%	1	0.4743	0.9904	84.95%	0.9851	0.1190	0.7624	84.5%	0.9819	0.1086	0.7448
极致梯度提升树模型	98.75%	1	0.92	0.9998	83.25%	0.9458	0.2216	0.7232	83.33%	0.9490	0.2114	0.7242
Logistic回归模型	84.77%	0.9936	0.0629	0.7282	84.59%	0.9926	0.0562	0.7257	84.77%	0.9947	0.0571	0.7183

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