# Applying Machine Learning Techniques to the Audit of Antimicrobial Prophylaxis
## 4 Discussion
For example, SimpleLogistic worked better than decision tree with sampling methods for the datasets of obstetrics and gynecology and urology, but not for neurosurgery dataset. There are several methods to solve this problem of imbalanced data, such as resampling the datasets by under-sampling the majority class and over-sampling the minority class, modifying algorithms, and considering a different perspective, such as anomaly [24; 25; 34]. We used two resampling approaches (Bagging and AdaBoost) to overcome the problem of imbalanced dataset. Bagging increased the precision for decision tree algorithm in this study. AdaBoost could reduce the bias of decision tree, but could be prone to overfitting noisy, as shown in Classes C and E [25]. Overfitting is a common problem in supervised machine learning algorithms. Overfitting occurs if the model performs perfectly on the training dataset, while fitting poorly on the testing dataset. Overfitting happens because of the presence of noise in the training dataset, small size of dataset, and the complexity of classifiers. Various strategies were proposed to reduce overfitting. The first is data sampling methods, including undersampling and oversampling [25; 35]. SpreadSubsample randomly removes instances from the majority class to make the dataset balanced. However, because undersampling method could lose useful data for classifiers, decision tree with SpreadSubsample did not achieve better performance than decision tree alone in this study. SMOTE is an oversampling method that has the advantage of no loss of useful data. SMOTE could improve the performance measures of decision tree in this study, as well as other studies [36; 37]. However, SMOTE may require additional computational time if the dataset is very large, as shown in Table 7, SMOTE took more execution time as compared with decision alone or SpreadSubsample [25; 37]. The other methods for reducing overfitting are cross-validation, ensemble methods, as shown in this study [25; 35]. However, overfitting cannot be avoided completely. As with most applications in data science and machine learning algorithms, there is no definitive best approach that always performs excellently. Depending on the characteristics of the dataset, distribution of classes, models, and predictions, some of the above algorithms will perform better than others  
It takes about 40 s to review the data of a single instance manually. The estimated execution time of manual review for all the 601 instances will be 24,040 s (6 h, 40 min and 40 s). However, the actual execution time of manual review will exceed 24,040 s, because we cannot continuously concentrate on reviewing the data without rest. Although they have better performance measures, Auto-WEKA and multilayer perceptron require more execution time as compared with that of other algorithms. All the algorithms were more efficient in execution time than manual review. Finally, this study demonstrated that multilayer perceptron, SimpleLogistic, and decision tree had outstanding discrimination with AUC > 0.9 for identifying the appropriateness of surgical prophylaxis, despite the imbalanced data. However, we still consider a different perspective that the rare instances are anomalies in the dataset. The antimicrobial use of these rare instances was inappropriate as compared to those regular practices which were according to the recommendations of antimicrobial prophylaxis guidelines. With the models determined by all the algorithms, we can easily identify the anomalies or outliers. There are some limitations in this study. First, for the instances with active infection already present at the time of surgery, the antimicrobial agents could then be selected by clinicians according to the clinical data, such as source of infection or microbiology. These antibiotics were used for active infections rather than surgical prophylaxis.