# Applying Machine Learning Techniques to the Audit of Antimicrobial Prophylaxis
## 4 Discussion
Therefore, gentamicin is used only if Gram-negative bacteria infection is a concern of postoperative infections and the patient is allergic to cefazolin. In comparison, antimicrobial agents compliant with guidelines were used for 92.6% of patients in U.S. hospitals [7] and 78.4% in Italian hospitals. [9] Prolonged surgical prophylaxis longer than 24 h was common in the worldwide surveys, including 54.2% in the European hospitals, 52.4% in the global survey, 45.7% in Australia hospitals, and 20.7% in US hospitals [8; 29; 30; 32]. Compared with that of surgical prophylaxis used for 24 h or less, prophylactic antimicrobial agent used for more than 24 h for most surgical procedures does not reduce postoperative infections but increases the risk of antimicrobial resistance and adverse effects. In the absence of preoperative infection or postoperative complications, prolonged postoperative antimicrobial prophylaxis is not necessary [2; 32]. International collaborative works are needed to improve appropriate antimicrobial use for surgical prophylaxis, such as optimal antibiotic choice, dosage, and length of prophylaxis. WHO suggests an assessment method for antimicrobial surgical prophylaxis by reviewing the essential variables for evaluation, including indication (type of surgery), age and gender of the patients, comorbidities, antibiotics prescribed as prophylaxis, dose, time of administration, and duration of antimicrobial use [11]. Manual method is not suitable for the audit of enormous data from annual survey in a medical center, interhospital comparison, or national survey. Auditing appropriate antimicrobial use for surgical prophylaxis by manual review of enormous medical data is labor-intensive and time-consuming. It is needed to develop efficient models for the audit of appropriateness of prophylactic antibiotic use. The purpose of this study was to investigate whether machine learning techniques could be used to predict the appropriate antimicrobial use for surgical prophylaxis from a large volume of clinical data. Machine learning techniques facilitate reviewing large volumes of data to discover specific patterns and trends that would usually not be apparent to humans. As shown in the results, with the help of the machine learning techniques, we could easily identify the five types of compliance with recommendations of antimicrobial prophylaxis. In addition, multilayer perceptron, SimpleLogistic, and decision tree algorithms provided high-performance measures for identifying appropriate use (Class A) and inappropriate use (Classes B, C, and D). The appropriateness of prophylactic antibiotic use by different surgical specialties can also be determined rapidly by the efficient models developed in this study. Imbalanced dataset contains instances that are unevenly distributed among the different classes. Between-class imbalance means unequal numbers of instances between different classes; within-class imbalance occurs when a class consists of different subclasses with unequal numbers of instances [33]. Both between-class imbalance and within-class imbalance are observed commonly in datasets. Between-class imbalances as well as within-class imbalances can affect classification performance. The between-class imbalance was observed in the dataset in this study, i.e., unequal distribution in each class. Classes C and E were much smaller subsets, as compared with that of Classes A and D. The performance metrics in Classes C and E were not as good as those of Classes A and D. There were also unequal sample sizes and minority instances within each labeled class, i.e., within-class imbalance. The sample sizes of minority instances were too small to be identified accurately by the machine learning tools, especially in Class E, as shown in Table 6.  
Imbalanced dataset is a common problem in machine learning classification. This imbalanced data can prevent the machine learning algorithms from building accurate models for these minority classes and lead to prediction errors [25; 34].