## 4 Results and Discussions
### Case Study
#### 4.1.2 Second Phase  
All those records which are identified as \"outlier\" or \"need to be investigated\" are forwarded to the Rule Engine for a further investigation. Total 62 association rules are generated from this data set and separate rule is generated for each specialization and specialty_id is used to represent identifier for each specialization. Rule engine basically generate rules that describe which specialization can provide which specific service. We generated rule by computing confidence values for each service in particular specialization. By using this knowledge, we can evaluate each transaction whether it is normal or fraud. This can be done by applying the Similarity function. We have selected specialty_id: 620 and we can get all services which are provided by this specialty_id as shown in Figure 12. It can be seen that there is confidence value of each service for each specialty_id. The relationship between service and specialty is depicted in Figure 13, in this plot confidence values of all service_ids for the specialty_ids are depicted. The value of confidence has provided us with an estimation, that what is the probability of prescription of considered service in this specialty_id. Based on this estimation, resources can be also allocated and budget can also be planned. Table 5, is depicting confidence values of few services for different specialties. Figure 11: Patient with respect to doctor association scores. Figure 12: The specialty_id 620 (Urology) is selected and confidence values of each service availed/provided in this specialty. Rule only contain service_ids whose confidence values are above 0.001 or user can define the threshold depending upon their scenarios. The rule generated by the rule engine can be explained with the help of example. Consider specialty name Pediatric Cardiology. Table 6 is showing services availed from this specialty and confidence values of these services are also provided. Table 7 is depicting rule for this specialty. If in any transaction Abdomen upper service is availed. The similarity function first check whether this service is present in the rule of Pediatric Cardiology as shown in Table 6. This case is identified as fraud, and if in any transaction service is availed whose confidence value is less than 0.001, from the considered specialty it will also be identified as fraud and passed to analyst dashboard for further investigation. The rules are generated from the medical historical data. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Service ID** & **Service Description** & **Confidence Values** \\ \hline
513 & LIPID Profile(HDL,LDL,Chlstrl,Trig) & 0.8660617 \\
9 & ECG 12 Lead & 0.5245009 \\
11 & Fetal Echo/Pediatric Echo & 0.6903811 \\
12 & ECHO 2D \& M Mode With Doppler & 0.5506352 \\
14 & ECHO F/U with in one week & 0.5219601 \\
15 & ECHO Transesophageal & 0.6388385 \\
16 & 24 Hour Holter & 0.5183303 \\
631 & Rx Services-OPD & 0.9303085 \\
1055 & Doctor-ID 16-OPd Initial Visit & 0.814519 \\
1056 & Doctor-ID 16-OPd Follow-up Visit & 0.7604356 \\
1119 & Doctor-ID 21-OPd Initial Visit & 0.9771325 \\
1120 & Doctor-ID 21-OPd Follow-up Visit & 0.8598911 \\
21 & 24-Hour Ambulatory B.P. Monitor & 0.6896552 \\
342 & Pelvis & 0.9814882 \\
487 & BUN & 0.9876588 \\
488 & Creatinine Serum & 0.9938294 \\
489 & Uric Acid Serum & 0.9967578 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Services and their confidence values for Pediatric Cardiology.