## 2 Related Work
Most of the times, the studies are supplemented with the prior knowledge of the medicines that were being used for the various diseases and they established a correlation between the reference set (the original knowledge) and the candidate set (the extracted knowledge) [27; 28]. 4. To identify the joint fraudsters, the clustering technique is applied. The similarity adjacency graph is used along with group mining for distinguishing the normal behaviour from abnormal behaviours [29]. The treatment model for different diseases, that of, assessing the doctors' trustworthiness, which is one of the critical metrics for detecting fraud at the provider level, is introduced [30]. The association rule mining is a very important technique which generates rules for the frequently occurring items. This technique is being utilized in many previous researches for generating rules from the domain knowledge provided by the domain's experts. This technique generates the rules out of which some are significant and some are insignificant [31]. There are two most useful parameters to analyze the strength of the association rules namely: confidence and support [32; 33; 34]. The characteristics like uniqueness, understandability, applicability and reliability for assessing the generated rules are discussed [35]. The classification of the insurance claims are performed by using the Genetic Support Vector Machine [36]. Table 1 is providing detailed comparison between the exisiting systems and the proposed framework. All aforementioned researches focus on disease correlations and medications whereas our proposed framework generates associations between the doctors, patients and services. Most of the existing studies use the domain knowledge to make the knowledge base but our system learns knowledge from the five years transactional data of a local hospital using the machine learning techniques. Based on this knowledge base, we classify the fraud cases. Most of the previous researches are based on the financial analysis for the detection of fraudulent activities but our research identifies anomalies using the association scores and performs the rule-engine analysis for the identification of the fraud cases. We analysed some of the most relevant researches with respect to data mining techniques used to detect types of frauds in Table 1. The comparative analysis highlights that most of the researches lack inclusion of all three key elements (doctors, patients, service) during analytical processing of data. The payment based analysis is utilized to detect patient level frauds and medication/disease associations are analyzed for detecting doctor level frauds. The most critical element missing in all these recent researches are services, which are either provided or availed. \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Frameworks and** **References** & **Data Mining Approach** & **Type of Detected Fraud** & **Applied Data Mining Technique (s)** \\ \hline GSVMs [36] & hybrid & classifying insurance claims & Genetic support vector machines \\ \hline Medical provider specialty predictions for anomaly detection [14] & Supervised & Physician related frauds & Multinomial Naive Bayes \\ \hline Fraud detection and frequent pattern matching [20] & Unsupervised & Disease based anomalies/frauds or period based claim related frauds & K means clustering \\ \hline Fraud detection using outlier predictor in health insurance data [27] & Hybrid & Disease, medication related frauds & Discrimination rule based outlier analysis using clustering and graph theory \\ \hline Healthcare fraud detection based on trustworthiness of doctors [30] & Hybrid & Provider (doctor) related fraud & Graph based mining frequent mining algorithms \\ \hline Fraudulent claims detection from expected payment deviations [17] & Supervised & Medicine payment related frauds & Regression models used \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison with Existing Literature.