## 5 Validation
In this approach, one recommendation is generated for each correlation, although, the recommendations which are based on patients' dissatisfaction are probably more useful than ones which are based on patients' satisfaction. 2. The associations can be used to produce target-based recommendations. Assume that a patient is being admitted to a hospital. The reception takes the patient's information and relative recommendations would be popped out. For instance, suppose an old patient with physician referral is being admitted for surgical reason. Based on the 4 th correlation in Table 4, a recommendation is shown to the health care provider asserting that this patient needs more information about her/his symptoms. This can be accomplished by a simple rule-based expert system. \begin{table}
\begin{tabular}{l l} \hline \hline
1 & Patients who have high Satisfaction with Symptoms Info, have these qualities: \\  & Mostly Physician Referral Admission Source, Mostly Obstetric Principal Reason of Admission \\ \hline \hline
2 & **z-test** & \(p\)-value = **5.57545804293131** \\ \hline
2 & Patients who have low Satisfaction with Symptoms Info, have these qualities: \\  & Mostly Young, Mostly Female, Mostly Physician Referral Admission Source, Mostly Obstetric Principal Reason of Admission \\ \hline \hline
3 & Patients who have high Satisfaction with Help After Discharge, have these qualities: \\  & Mostly Physician Referral Admission Source, Rarely Medical Principal Reason of Admission \\ \hline
4 & Mostly Old, Mostly Physician Referral Admission Source, Mostly Surgical Principal Reason of Admission \\ \hline
5 & Patients who have high Satisfaction with Help After Discharge, have these qualities: \\  & Mostly Physician Referral Admission Source, Mostly Obstetric Principal Reason of Admission \\ \hline
2 & **z-test** & \(p\)-value = **3.59338301942709** \\ \hline
5 & Patients who have high Satisfaction with Help After Discharge, have these qualities: \\  & Mostly Physician Referral Admission Source, Mostly Obstetric Principal Reason of Admission \\ \hline
2 & **z-test** & \(p\)-value = **3.50130627065847** \\ \hline
6 & Patients who have low Satisfaction with Help After Discharge, have these qualities: \\  & Mostly Young, Mostly Female, Mostly Physician Referral Admission Source, Mostly Obstetric Principal Reason of Admission \\ \hline
2 & **z-test** & \(p\)-value = **3.39514132553282** \\ \hline
7 & Patients who have high Satisfaction with Help After Discharge, have these qualities: \\  & Mostly Old, Mostly Emergency Room Admission Source, Mostly Medical Principal Reason of Admission \\ \hline
8 & Patients who have high Satisfaction with Help After Discharge, have these qualities: \\  & Mostly Old, Mostly Emergency Room Admission Source \\ \hline
2 & **z-test** & \(p\)-value = **-2.85471982135127** \\ \hline
9 & Patients who have high Satisfaction with Help After Discharge, have these qualities: \\  & Rarely not Spanish/Hispanic/Latino Ethnicity, Rarely English Language \\ \hline
2 & **z-test** & \(p\)-value = **2.45951143035353** \\ \hline
10 & Patients who have high Satisfaction with Symptoms Info, have these qualities: \\  & Mostly Old, Mostly Emergency Room Admission Source, Mostly Medical Principal Reason of Admission \\ \hline
2 & **z-test** & \(p\)-value = **-0.21282628801027** \\ \hline \hline \end{tabular}
\end{table} 
Table 16: The list of cleaned associations ranked based on their odds ratio. ## 7 Conclusions

In this work, an unsupervised exploratory data analysis methodology is introduced to discover associations between patients' demographics and their various satisfaction identifiers. Such associations are extracted using a two-layer cluster analysis together with extracting the salient features of each cluster. The associations are validated using statistical tests and are ranked based on their significance. The goal was to use such associations to create a patient satisfaction based the recommendation system for hospitals.