Harnessing Machine Learning for Quantifying Vesicoureteral Reflux: A Promising Approach for Objective Assessment

Muhyeeddin Alqaraleh, M. Alzboon, M. Al-Batah, Mutaz Abdel Wahed, Ahmad Abuashour, Firas Hussein Alsmadi
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Abstract

In this study, we evaluated the performance of various machine-learning models on multiple datasets labeled GR1, GR2, GR3, GR4, and GR5. We assessed the models using a range of evaluation metrics, including AUC, CA, F1, precision, recall, MCC, specificity, and log loss. The models examined were logistic regression, decision tree, kNN, random forest, gradient boosting, neural network, AdaBoost, and stochastic gradient descent. The results indicate that all models consistently demonstrated outstanding performance across all datasets, with most achieving perfect scores in all metrics. The models exhibited high accuracy and effectiveness in accurately classifying instances. Although random forests displayed slightly lower scores in some metrics, theyi still maintained an overall high level of accuracy. The findings highlight the models’ ability to effectively learn the underlying patterns within the data and make accurate predictions. The low log loss values further confirmed the models’ precise estimation of probabilities. Consequently, these models possess strong potential for practical applications in various domains, offering reliable and robust classification capabilities.
利用机器学习量化膀胱输尿管反流:客观评估的有效方法
在本研究中,我们评估了标有 GR1、GR2、GR3、GR4 和 GR5 的多个数据集上各种机器学习模型的性能。我们使用一系列评估指标对模型进行了评估,包括 AUC、CA、F1、精确度、召回率、MCC、特异性和对数损失。考察的模型包括逻辑回归、决策树、kNN、随机森林、梯度提升、神经网络、AdaBoost 和随机梯度下降。结果表明,所有模型在所有数据集上都表现出了卓越的性能,其中大多数模型在所有指标上都获得了满分。这些模型在对实例进行准确分类方面表现出很高的准确性和有效性。虽然随机森林在某些指标上的得分略低,但总体上仍保持了较高的准确率。这些发现凸显了模型有效学习数据中潜在模式并做出准确预测的能力。低对数损失值进一步证实了模型对概率的精确估计。因此,这些模型具有在各个领域实际应用的强大潜力,能够提供可靠、稳健的分类能力。
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