目的:应用不同的机器学习算法,利用术前血液参数数据预测经尿道前列腺电切术(TURP)后尿道狭窄的概率。
方法:回顾性分析包括患者特征在内的接受双极TURP的患者数据,术前血常规检查结果,术后尿流图用于开发和教育机器学习模型。各种指标,比如F1得分,模型精度,负预测值,正预测值,灵敏度,特异性,尤登指数,ROCAUC值,和每个模型的置信区间,用于评估机器学习模型对尿道狭窄发展的预测性能。
结果:实施严格的纳入和排除标准后,共纳入109例患者数据(55例无尿道狭窄患者和54例尿道狭窄患者)。术前血小板分布宽度,平均血小板体积,Plateletcrit,活化部分凝血活酶时间,和凝血酶原时间值在两个队列之间具有统计学意义。将数据应用于机器学习系统后,不同算法的精度预测得分如下:决策树(0.82),逻辑回归(0.82),随机森林(0.91),支持向量机(0.86),K-最近邻(0.82),和朴素贝叶斯(0.77)。
结论:我们的机器学习模型在预测TURP后尿道狭窄概率方面的准确性已经证明了显著的成功。探索整合补充变量的前瞻性研究有可能提高机器学习模型的精度和准确性,因此,提高了他们预测TURP后尿道狭窄风险的能力。
OBJECTIVE: To predict the post transurethral prostate resection(TURP) urethral stricture probability by applying different machine learning algorithms using the data obtained from preoperative blood parameters.
METHODS: A retrospective analysis of data from patients who underwent bipolar-TURP encompassing patient characteristics, preoperative routine blood test outcomes, and post-surgery uroflowmetry were used to develop and educate machine learning models. Various metrics, such as F1 score, model accuracy, negative predictive value, positive predictive value, sensitivity, specificity, Youden Index, ROC AUC value, and confidence interval for each model, were used to assess the predictive performance of machine learning models for urethral stricture development.
RESULTS: A total of 109 patients\' data (55 patients without urethral stricture and 54 patients with urethral stricture) were included in the study after implementing strict inclusion and exclusion criteria. The preoperative Platelet Distribution Width, Mean Platelet Volume, Plateletcrit, Activated Partial Thromboplastin Time, and Prothrombin Time values were statistically meaningful between the two cohorts. After applying the data to the machine learning systems, the accuracy prediction scores for the diverse algorithms were as follows: decision trees (0.82), logistic regression (0.82), random forests (0.91), support vector machines (0.86), K-nearest neighbors (0.82), and naïve Bayes (0.77).
CONCLUSIONS: Our machine learning models\' accuracy in predicting the post-TURP urethral stricture probability has demonstrated significant success. Exploring prospective studies that integrate supplementary variables has the potential to enhance the precision and accuracy of machine learning models, consequently progressing their ability to predict post-TURP urethral stricture risk.