人工神经网络及Logistic回归模型对预测体外冲击波治疗上尿路结石的疗效分析

目的 探讨人工神经网络和Logistic回归模型对预测体外冲击波治疗上尿路结石的治疗效果预测.方法 从2010年1月至2015年1月,本院泌尿科共接受ESWL治疗的肾结石患者340例,将治疗前的病例资料10项(年龄大小、体重指数大小、病程时间长短、性别、尿路刺激症、血尿、肾绞痛、结石位置、患侧和大小)纳入预测参数,建立人工神经网络和Logistic回归模型,预测体外冲击波治疗上尿路结石的临床疗效.结果 人工神经网络得到预测参数重要性的前5位依次为结石大小、病程时间、血尿、结石位置、体重指数,进行显著性检验时,P＜ 0.05.Logistic回归模型中重要的参数分别为病程时间、血尿和结石位置,差异有统计学意义,P＜ 0.05.结论 人工神经网络和Logistic回归模型预测ESWL治疗上尿路结石成功率有较好的准确性,可以在临床上广泛推广.

Role of artificial neural network and logistic regression model in predicting effect of extracorporeal shock wave for upper urinary tract calculi

Objective To explore the role of artificial neural network and logistic regression model in predicting the effect of extracorporeal shock wave for upper urinary tract calculi.Methods From January,2010 to January,2015,d 340 patients with renal calculus were treated by ESWL at our hospital.The predictive parameters were sex,symptoms induced by urethral irritation,blood urine,renal colic,stone position,stone of one side,age,BMI,disease course,and stone size.Artificial neural network and logistic regression model were built basing on these parameters to predict the clinical effect of ESWL for calculus of upper urinary tract.Results The most important five parameters in artificial neural network were stone size,disease course,blood urine,stone position,and BMI,with statistical differences (P＜0.05).The most important parameters in logistic regression model were disease course,blood urine,and stone position,with statistical differences (P＜0.05).Conclusions Artificial neural network and logistic regression model in predicting the effect of extracorporeal shock wave for upper urinary tract calculi are both highly accurate,so both are worth being clinically generalized.