# Applying Machine Learning Techniques to the Audit of Antimicrobial Prophylaxis
## 3 Results  
The distributions of values of class label attribute with regard to choice and duration of prophylactic antimicrobial agents for the whole dataset are listed in Table 4. In Class A, cefazolin was the most common prophylactic antimicrobial agent and compliant with recommendations of guideline for the correct choice and duration. In Class B, cefazolin, cefoxitin, clindamycin, and ciprofloxacin were correctly chosen for different surgical procedures with risks of infections. However, the duration was longer than one day and not compliant with recommended duration of antimicrobial prophylaxis. In Class C, the combination of cefazolin and gentamicin was identified as the most common type of non-compliance with choice of antimicrobial agent, because there were no special clinical indications for prophylaxis of Gram-negative bacterial infections for their surgical procedures. In Class D, the combination of cefazolin and gentamicin was identified as the most common type of non-compliance with the correct choice and duration of antimicrobial agent. In Class E, the prescriptions of ampicillin-sulbactam and third-generation cephalosporers were classified as not compliant with the recommended choice of antimicrobial agents. Their indications were identified as treatment of other infections rather than prophylaxis. The performance metrics of machine learning techniques for the whole dataset are shown in Table 5. Classification accuracy alone can be misleading if there is an unequal number of observations in each class (type) in the dataset. Therefore, we had to look at other performance metrics in the Table 5. Multilayer perceptron had the best performance measures. SimpleLogistic was selected by Auto-WEKA as the optimal classifier for the whole dataset. However, the performance measures of SimpleLogistic were the second best among the algorithms in this study. The weighted average sensitivity, specificity, precision, and AUC of decision tree were 0.932, 0.972, 0.931, and 0.985 respectively. Bagging with decision tree, AdaBoost with decision tree, decision tree with SMOTE could increase the performance measures of decision tree, except the AUC of AdaBoost was a little less than that of decision tree. The performance measures of decision tree with SpreadSubsample were lower than those of decision tree alone. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Class**} & **No. of** & \multirow{2}{*}{**Choice of Antibiotics**} & **Duration of Prophylactic** \\  & **Instances** & & **Antimicrobial Use a ** \\  & **Instances** & & **(No. of Instances)** \\ \hline A & Total 255 & & \\  & 245 & Cefazolin & SP1 (178), SP2 (67) \\  & 5 & Cefoxitin & SP1 \\  & 4 & Cefoxitin & SP2 \\  & 1 & Cefuroxime + metronidazoleazole & SP2 \\ B & Total 155 & & \\  & 126 & Cefazolin & SP3 \\  & 8 & + other antibiotic (for different & SP3 \\  & & surgical procedures or risks) & \\  & 10 & Cefoxitin & SP3 \\  & 8 & Clindamycin & SP3 \\  & 3 & Ciprofloxacin & SP3 \\ C & Total 30 & & \\  & 25 & Cefazolin + gentamicin & SP1 (13), SP2 (12) \\  & 5 & Other antibiotics & SP1 (2), SP2 (3) \\ D & Total 103 & & \\  & 69 & Cefazolin + gentamicin & SP3 \\  & 5 & Cefoxitin + gentamicin & SP3 \\  & 2 & Cefoxitin & SP3 \\  & 21 & Oral cephalnexin & SP3 \\  & 6 & Others & \\ E & Total 57 & & \\  & 17 & Ampicillin–sulbactam & SP2 (4), SP3 (13) \\  & 10 & Third-generation cephalosporers & SP2 (4), SP3 (6) \\  & 30 & Others & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Choice and duration of prophylactic antimicrobial agents for whole dataset. The performance metrics (sensitivity, specificity, precision, and AUC) of the seven algorithms for the whole dataset (601 records) were shown in Figure 1. The seven algorithms (decision tree with SpreadSubsample, decision tree, Bagging with decision tree, Adaboost with decision tree, decision tree with SMOTE, SimpleLogistic, and multilayer perceptron) are plotted in increasing order of performance with regard to sensitivity, specificity, and precision.