Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran.
USERN Office, Fasa University of Medical Sciences, Fasa, Iran.
BMC Res Notes. 2024 Oct 24;17(1):318. doi: 10.1186/s13104-024-06979-2.
To enhance the identification of individuals at risk of developing clinically significant kidney stones.
In this study, data from the Fasa Adults Cohort Study were analyzed to explore factors linked to symptomatic and clinically significant kidney stone disease. After cleaning, 10,128 participants with 103 variables were studied. One outcome variable (presence of symptomatic kidney stones) and 102 predictor variables from surveys and tests were assessed. Five Machine learning (ML) algorithms (SVM, RF, KNN, GBM, XGB) were applied to examine kidney stone factors, with performance comparisons made. Data balancing was done using SMOTE, and metrics like accuracy, precision, sensitivity, specificity, F1 score, and AUC were evaluated for each algorithm.
The XGB model outperformed others with AUC of 0.60, while RF, GBM, SVC, and KNN had AUC values of 0.58, 0.57, 0.54, and 0.52. RF, GBM, and XGB showed good accuracy at 0.81, 0.81, and 0.77. Top predictors for kidney stones were serum creatinine, salt intake, hospitalization history, sleep duration, and BUN levels.
ML models show promise in evaluating an individual's risk of developing painful kidney stones and recommending early lifestyle changes to reduce this risk. Further research can enhance predictive accuracy and tailor interventions for better prevention/management.
提高识别有发生临床显著肾结石风险个体的能力。
本研究对法萨成年人队列研究的数据进行分析，以探讨与症状性和临床显著肾结石疾病相关的因素。在清理后，研究了 10128 名参与者的 103 个变量。评估了一个结局变量（存在症状性肾结石）和来自调查和检测的 102 个预测变量。应用 5 种机器学习（ML）算法（SVM、RF、KNN、GBM、XGB）来检查肾结石因素，并进行性能比较。使用 SMOTE 进行数据平衡，评估每个算法的准确性、精度、敏感性、特异性、F1 评分和 AUC 等指标。
XGB 模型的 AUC 为 0.60，优于其他模型，而 RF、GBM、SVC 和 KNN 的 AUC 值分别为 0.58、0.57、0.54 和 0.52。RF、GBM 和 XGB 的准确性较好，分别为 0.81、0.81 和 0.77。肾结石的主要预测因素是血清肌酐、盐摄入量、住院史、睡眠时间和 BUN 水平。
ML 模型在评估个体发生疼痛性肾结石的风险以及推荐早期生活方式改变以降低这种风险方面显示出潜力。进一步的研究可以提高预测准确性，并为更好的预防/管理量身定制干预措施。
