Abstract：
We assessed the efficacy of oversampling techniques to enhance machine learning model performance in predicting Escherichia coli MG1655 presence in spinach wash water. Three oversampling methods were applied to balance two datasets, forming the basis for training random forest (RF), support vector machines (SVMs), and binomial logistic regression (BLR) models. Data underwent method-specific centering and standardization, with outliers replaced by feature-specific means in training datasets. Testing occurred without these preprocessing steps. Model hyperparameters were optimized using a subset of testing data via 10-fold cross-validation. Models were trained on full datasets and tested on newly acquired spinach wash water samples. Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling approach (ADASYN) achieved strong results, with SMOTE RF reaching an accuracy of 90.0%, sensitivity of 93.8%, specificity of 87.5%, and an area under the curve (AUC) of 98.2% (without data preprocessing) and ADASYN achieving 86.55% accuracy, 87.5% sensitivity, 83.3% specificity, and a 92.4% AUC. SMOTE and ADASYN significantly improved (p < 0.05) SVM and RF models, compared to their non-oversampled counterparts without preprocessing. Data preprocessing had a mixed impact, improving (p < 0.05) the accuracy and specificity of the BLR model but decreasing the accuracy and specificity (p < 0.05) of the SVM and RF models. The most influential physiochemical feature for E. coli detection in wash water was water conductivity, ranging from 7.9 to 196.2 µS. Following closely was water turbidity, ranging from 2.97 to 72.35 NTU within this study.
摘要：
我们评估了过采样技术在预测菠菜洗涤水中大肠杆菌MG1655存在时增强机器学习模型性能的功效。三种过抽样方法被用来平衡两个数据集，形成训练随机森林(RF)的基础，支持向量机(SVM)，和二项逻辑回归(BLR)模型。数据经历了特定方法的集中和标准化，在训练数据集中，将异常值替换为特定于特征的方法。在没有这些预处理步骤的情况下进行测试。通过10倍交叉验证使用测试数据的子集优化模型超参数。模型在完整数据集上进行训练，并在新获得的菠菜洗涤水样品上进行测试。合成少数过采样技术(SMOTE)和自适应合成采样方法(ADASYN)取得了较好的效果,SMOTERF达到90.0%的精度，灵敏度为93.8%，特异性为87.5%，曲线下面积（AUC）为98.2%（未进行数据预处理），ADASYN准确度为86.55%，灵敏度87.5%,83.3%的特异性，和92.4%的AUC。SMOTE和ADASYN显著改善了(p<0.05)SVM和RF模型，与未经预处理的非过采样同行相比。数据预处理产生了喜忧参半的影响，提高了BLR模型的准确性和特异性（p<0.05），但降低了SVM和RF模型的准确性和特异性（p<0.05）。对洗涤水中大肠杆菌检测影响最大的理化特性是水的电导率,范围从7.9到196.2µS。紧随其后的是水的浊度，本研究范围为2.97至72.35NTU。
