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Due to the imbalanced distribution of business data, missing of user features and many other reasons, directly using big data techniques on realistic business data tends to deviate from the business goals. It is difficult to model the insurance business data by classification algorithms like Logistic Regression and SVM etc. This paper exploits a heuristic bootstrap sampling approach combined with...
Fraud and abuse are two factors directly related to high health care costs, since they correspond to expenses that can be eliminated without prejudice to the quality of services provided. In Brazil, the health insurance companies implement a claim authorization process which assists in the detection of fraud and abuse. This process consists of a prior analysis of the services requested by providers,...
Mortality analytics is an emerging research area that discovers and communicates meaningful patterns in clinical data to reduce mortality rates. Nonetheless, intensive care unit (ICU) mortality analytics for leading causes, such as circulatory system diseases (CDS), is still complicated due to the interactions of different mortality causes. To improve analytics accuracy and quality, clustering analysis...
In the medical decision field, the conventional analysis methods of medical insurance data are lack of flexibility and efficiency. When coping with huge and redundant medical dataset, it is difficult to extract the candidate attributes if only using some limited professional knowledge. Therefore, the correlations between the medical insurance cost and relevant factors (e.g. diagnosis results) need...
Recently there has been an evidential growth in E-healthcare services. Every hospital has a variety of similar or dissimilar healthcare services. Selecting the best healthcare service is influenced by many preferences such as doctor's experience, location, feedback on continuity of care, waiting time, cost, hospital facilities, etc. Among the preferences, feedback is more influential. Participant's...
Big data is now rapidly expanding into various domains such as banking, insurance and e-commerce. Data analysis and related studies have attracted more attentions. In health insurance, abuse of diagnosis is one of the key fraud problems, which damages the interests of insured people. To address this issue, numbers of studies have focused on this topic. This paper develops a healthcare fraud detection...
Fraud detection is interesting research topic and it not only needs data mining techniques but also needs a lot of inputs from domain experts. In health care claims, relationships between physicians and patients form complex communities structures and these communities could lead to potential fraud discoveries. Traditionally, researchers have focused on clustering physicians and patients and tried...
With the rapid development of clustering analysis technology, there have been many application-specific clustering algorithms, such as text clustering. K-Means algorithm, as one of the classic algorithms of clustering algorithms, and a textual document clustering algorithms commonly used in the analysis process, is widely used because of its simple and low complexity. This article in view of two big...
The explosive growth of the insurance data puts forward a higher demand for the data processing of the internet of things. This paper adopts distributed database as the underlying data storage, combines with array-based apriori technology on the basis of data mining, applies and queries, and proposes a type of array-based apriori optimization algorithm. Array-based data layout, first stacks data arrays,...
Currently, insurance fraud spreads quickly in the domestic and foreign field, especially in the field of automobile insurance, so that we need more efficient and accurate technology to anti automobile insurance fraud. Therefore, this paper studied the data mining technology to anti automobile insurance fraud. The improved outlier detection method based on the nearest neighbor with pruning rules was...
Structure learning of Bayesian Network (BN) is one of important topics in machine learning and widely applied in expert system. The traditional algorithms for structure learning are usually focused on the batch data in nature. It is difficult to learn the structure quickly from the huge amounts of data. But in many practical applications, the structure of BN should be learned by using time-series...
Sequence data are increasingly shared to enable mining applications, in various domains such as marketing, telecommunications, and healthcare. This, however, may expose sensitive sequential patterns, which lead to intrusive inferences about individuals or leak confidential information about organizations. This paper presents the first permutation-based approach to prevent this threat. Our approach...
Based on the analysis the advantages and disadvantages of hierarchical clustering and fuzzy clustering methods, make sample points within each subspace as similar as possible, differences between sample points within different sub-space as large as possible, The essence is to find different data models which hidden in different data. It is an unsupervised learning process that enables the blind classification...
This study attempts to develop a new method that could be used to handle the problem of finding the cut point or interval of continuous-valued attribute in decision tree, and to reach the following objectives: 1. The decision tree algorithm can handle the data that combines both the nominal attribute and continuous-valued attribute. 2. The decision tree algorithm has less nodes and branches in the...
In the modern age, people enjoy the advantages coming from the information technology. At the same time, all kinds of problems of information security appear. Fingerprint recognition technology more practical as the current biometric technology has become a hot research at home and abroad. Traditional PC-based fingerprint identification system as poor mobility, the disadvantages limit its power in...
An important facility to aid keyword search on XML data is suggesting alternative queries when user queries contain typographical errors. Query suggestion thus can improve users' search experience by avoiding returning empty result or results of poor qualities. In this paper, we study the problem of effectively and efficiently providing quality query suggestions for keyword queries on an XML document...
Automatic identification technology becomes a urgent need of production and life, authentication technology gained worldwide attention because of its high reliability, fingerprint identification technology which applied to social security system can accurately determine protects a person's identity and prevent the phenomenon of the pension of falsely claim that solves this one long-term puzzling problem...
The HITON_PC algorithm which is a state-of-the-art local causal discovery algorithm can deal with a dataset with a very small sample-to-variable ratio efficiently. But it cannot perform inefficiently on a dataset with a very large sample. To address this problem, a fast HITON_PC algorithm is presented which uses a new yet simple search strategy from high order to low order to improve the efficiency...
In this paper, a new hybrid incremental learning algorithm for Bayesian network structures is proposed. It develops a polynomial-time constraint-based technique to build up a candidate parents set for each domain variable, and a hill climbing search procedure is then employed to refine the current network structure under the guidance of those candidate parents sets. Our algorithm always offers considerable...
Nowadays, expansion of insurance companies provides competitive market, in which several companies participate in tenders with huge price. as a lot of tenders are held based on electronic form, tender documents security is taken into consideration as high level technical issue. In this paper, decision making algorithm based on AHP for suitable authentication function in e-tender of insurance companies...
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