## 3 Classification Framework for Analysis
### Advantages and Drawbacks for the Healthcare Sector
With the constant growth in data volume, an accompanying increase comes in the difficulty of detecting fraudulent activity. Detecting fraudulent claims in the health insurance industry has been an area of focus for a number of researchers [17; 89; 91; 92; 93; 94; 95]. In their article from 2015, [PERSON] and [PERSON] [89] propose a hybrid system that detects duplicate insurance claims by combining the methods of clustering and classification. The authors point out that classification algorithms and clustering approaches on their own are not enough to identify duplicate claims. The authors suggest a model that consists of two stages as a solution to this problem. The first thing that hasto be done is some claim clustering, which should be done using the evolving clustering approach. The output of the first step is used as input for the second phase, which is based on the SVM algorithm and uses the classification step. [PERSON] et al. [17] provide a structure for a health insurance system that makes use of extreme gradient acceleration (XGBoost). The goal of this framework is to reduce the amount of human interaction required, secure insurance activities, alert and notify consumers who pose a danger, identify erroneous information, and cut down on revenue losses for the insurance industry. When the XGBoost algorithm was used to analyze data from a car insurance dataset, the findings showed that it attained considerable performance increases in comparison to other learning algorithms that are currently in use. These are just a few examples of how recent breakthroughs in AI have brought benefits to firms that have used AI systems [96; 97; 98; 99]. There are a great number of benefits that can be brought to the realm of healthcare by expanding the use of AI technologies to this important sector. It helps lower the amount of time and money spent on therapy as well as the amount of resources that are used [100]. In addition, it shortens the amount of time needed to make a diagnosis and, as a result, the process of making a decision. This has a significant effect on treatment strategies and outcomes, and it may even be lifesaving in extreme circumstances. When carried out by a variety of healthcare professionals at a variety of health facilities, data exchange in the healthcare industry is an important component of individual wellbeing. In addition to this, it is essential for the development of scientific research. The article by [PERSON] et al. [97] promotes the utilization of AI for educational purposes and presents a model which integrates AI into the training program for medical professionals. AI has the potential to be beneficial across the board in the healthcare industry. The processing of certain AI algorithms requires a significant amount of data. Due to the ethical implications of such data, it can be challenging to gather data at times, particularly data pertaining to patients. If some classification and clustering methods are applied to a very small amount of data, the results may be quite accurate; nevertheless, this may not be practical or useful [60; 64; 84]. For AI methods to work well, the data must first be preprocessed. In particular, natural language processing must be performed extensively on text data before they can be used. One of the most difficult issues in medical data processing is the need to integrate several forms of data using the same algorithm at times [60; 64; 84]. This is one of the reasons why there are so many different types of algorithms. Data can be gathered for medical purposes from a wide variety of sources and forms, including medical imaging, 3D video sequences, still photographs, and quantitative data. The analysis of healthcare data presents many challenges, one of which is the collection of accurate, trustworthy, and effective data. The majority of AI's applications in healthcare are focused on improving the diagnostic process. Incorrect conclusions reached through computerized diagnosis could have extremely negative consequences.