# Application of Association Rules Analysis in Mining Adverse Drug Reaction Signals
## 4 Results
### Comparison of the Mhra Method and the ARA Method at the Performance Level  
However, the best thresholds obtained from the overall sample and the performance they exhibited were the best results that the ARA method could achieve. In order to obtain the average performance level of the ARA method, we used 10-fold cross-validation to evaluate the performance of our method. We divided the dataset into 10 subsets on average, selected 9 of them as the training set and the other as the test set, and performed 10 experiments without repeating them. In the training set, we used the ARA method to obtain the optimal thresholds, then used them as the optimal thresholds in the test set, and evaluated the performance indicators of the thresholds which we obtained from training set in the test set. When we determined the thresholds of the metrics used in the training \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**F1** & **Lift = 1** & **Lift = 1.1** & **Lift = 1.2** & **Lift = 1.3** & **Lift = 1.4** & **Lift = 1.5** \\ \hline Confidence = 0.006 & 40.28\% & 39.50\% & 38.83\% & 38.17\% & 37.65\% & 37.07\% \\ Confidence = 0.007 & 40.51\% & 39.71\% & 38.98\% & 38.27\% & 37.71\% & 37.15\% \\ Confidence = 0.008 & 40.49\% & 39.66\% & 38.93\% & 38.21\% & 37.62\% & 37.01\% \\ Confidence = 0.009 & 40.35\% & 39.52\% & 38.76\% & 38.07\% & 37.45\% & 36.82\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The value of F1(Finally). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**F1** & **Lift = 1** & **Lift = 1.5** & **Lift = 2** & **Lift = 2.5** & **Lift = 3** \\ \hline Confidence = 0.005 & 39.91\% & 36.85\% & 34.40\% & 32.04\% & 30.38\% \\ Confidence = 0.010 & 40.30\% & 36.77\% & 33.74\% & 30.94\% & 29.03\% \\ Confidence = 0.015 & 39.50\% & 35.73\% & 32.70\% & 29.70\%  
set, we could divide each piece of data in the test set into four categories: TP, TN, FP, and FN based on the threshold, and then we used Formulas (6)-(8) to obtain the performance of the ARA method to detect ADR signals through the simulation experiments and found the average performance of ten experiments. We used the three average performance indicators obtained from ten experiments as the final result of the threshold determination, respectively: Precision = 36.28%, Recall = 45.65%, and F1 = 40.38%. Figure 1 is a comparison of the performance of the ARA method and the MHRA method. As shown in Figure 1, Precision was reduced from 40.41% in the MHRA method to 36.28% in the ARA method; Recall increased from 25.72% in the MHRA method to 45.65% in the ARA method; F1 increased from 31.43% in the MHRA method to 40.38% in the ARA method. As can be seen from Section 3.4, we used F1 as the main indicator to judge the performance of ADR signal detection, and the F1 of the ARA method was increased by 28.48% compared to the F1 of the MHRA method. These results showed that the performance of the ARA method was much better than that of the MHRA method. We used the data of levofloxacin in the dataset to concretely characterize the superior performance of ARA. We screened the top ten potential ADRs that may be caused by levofloxacin from the dataset and reported the results in Table 5 (the frequency of adverse symptoms is in descending order). \"Has been identified as a positive signal\" means the ADR was determined as the drug's ADR. \"Detected as a positive signal by MHRA method\" means that the ADR was detected as an ADR signal by the MHRA method. \"Detected as a positive signal by ARA method\" means that the ADR was detected as an ADR signal by the ARA method. Figure 1: Comparison of the performance of the MHRA method and the ARA method. In this dataset, the number of rashes caused by levofloxacin was 7088, accounting for about 15.60% of the ADRs caused by the drug. Moreover, a rash was determined as levofloxacin's ADR, but the MHRA method did not detect this as an ADR signal, while the ARA method detected it as an ADR signal.