scholarly journals An Intelligent Data Mining System to Detect Healthcare Fraud

2011 ◽  
pp. 149-168 ◽  
Author(s):  
Guisseppi A. Forgionne ◽  
Aryya Gangopadhyay ◽  
Monica Adya
Keyword(s):  
Data Mining ◽  
Fraud Detection ◽  
Financial Impact ◽  
Classification Rules ◽  
Mining System ◽  
Quality Healthcare ◽  
Proactive Approach ◽  
Care Industry ◽  
Use Of Data ◽  
The Cost

There are various forms of fraud in the health care industry. This fraud has a substantial financial impact on the cost of providing healthcare. Money wasted on fraud will be unavailable for the diagnosis and treatment of legitimate illnesses. The rising costs of and the potential adverse affects on quality healthcare have encouraged organizations to institute measures for detecting fraud and intercepting erroneous payments. Current fraud detection approaches are largely reactive in nature. Fraud occurs, and various schemes are used to detect this fraud afterwards. Corrective action then is instituted to alleviate the consequences. This chapter presents a proactive approach to detection based on artificial intelligence methodology. In particular, we propose the use of data mining and classification rules to determine the existence or non-existence of fraud patterns in the available data. The chapter begins with an overview of the types of healthcare fraud. Next, there is a brief discussion of issues with the current fraud detection approaches. The chapter then develops information technology based approaches and illustrates how these technologies can improve current practice. Finally, there is a summary of the major findings and the implications for healthcare practice.

scholarly journals Data Mining Usage in Corporate Information Security: Intrusion Detection Applications

2017 ◽  
Vol 8 (1) ◽  
pp. 51-59 ◽  
Author(s):  
Masoud Al Quhtani
Keyword(s):  
Data Mining ◽  
Information Security ◽  
Intrusion Detection ◽  
Mining System ◽  
Systematic Analysis ◽  
New Methods ◽  
Use Of Data ◽  
Efficient Data ◽  
Abuse Risk ◽  
Corporate Information

AbstractBackground: The globalization era has brought with it the development of high technology, and therefore new methods of preserving and storing data. New data storing techniques ensure data are stored for longer periods of time, more efficiently and with a higher quality, but also with a higher data abuse risk. Objective: The goal of the paper is to provide a review of the data mining applications for the purpose of corporate information security, and intrusion detection in particular. Methods/approach: The review was conducted using the systematic analysis of the previously published papers on the usage of data mining in the field of corporate information security. Results: This paper demonstrates that the use of data mining applications is extremely useful and has a great importance for establishing corporate information security. Data mining applications are directly related to issues of intrusion detection and privacy protection. Conclusions: The most important fact that can be specified based on this study is that corporations can establish a sustainable and efficient data mining system that will ensure privacy and successful protection against unwanted intrusions.

Fault Diagnosis of Automobile ECUs with Data Mining Technologies

2010 ◽  
Vol 40-41 ◽  
pp. 156-161 ◽  
Author(s):  
Yang Li ◽  
Yan Qiang Li ◽  
Zhi Xue Wang
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Data Stream ◽  
Electronic Control Unit ◽  
Rapid Development ◽  
Reliability And Validity ◽  
Control Unit ◽  
Use Of Data ◽  
The Cost

With the rapid development of automotive ECUs(Electronic Control Unit), the fault diagnosis becomes increasingly complicated. And the link between fault and symptom becomes less obvious. In order to improve the maintenance quality and efficiency, the paper proposes a fault diagnosis approach based on data mining technologies. By making full use of data stream, we firstly extract fault symptom vectors by processing data stream, and then establish a diagnosis decision tree through the ID3 decision tree algorithm, and finally store the link rules between faults and the related symptoms into historical fault database as a foundation for the fault diagnosis. The database provides the basis of trend judgments for a future fault. To verify this approach, an example of diagnosing faults of entertainment ECU is showed. The test result testifies the reliability and validity of this diagnostic method and reduces the cost of ECU diagnosis.

Predicting effects of selected impregnation processes on the observed bending strength of wood, with use of data mining models

BioResources ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. 4891-4904
Author(s):  
Selahattin Bardak ◽  
Timucin Bardak ◽  
Hüseyin Peker ◽  
Eser Sözen ◽  
Yildiz Çabuk
Keyword(s):  
Data Mining ◽  
Goodness Of Fit ◽  
Bending Strength ◽  
Wood Preservation ◽  
Ambient Conditions ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
The Cost ◽  
Mining Algorithms ◽  
The Relationship

Wood materials have been used in many products such as furniture, stairs, windows, and doors for centuries. There are differences in methods used to adapt wood to ambient conditions. Impregnation is a widely used method of wood preservation. In terms of efficiency, it is critical to optimize the parameters for impregnation. Data mining techniques reduce most of the cost and operational challenges with accurate prediction in the wood industry. In this study, three data-mining algorithms were applied to predict bending strength in impregnated wood materials (Pinus sylvestris L. and Millettia laurentii). Models were created from real experimental data to examine the relationship between bending strength, diffusion time, vacuum duration, and wood type, based on decision trees (DT), random forest (RF), and Gaussian process (GP) algorithms. The highest bending strength was achieved with wenge (Millettia laurentii) wood in 10 bar vacuum and the diffusion condition during 25 min. The results showed that all algorithms are suitable for predicting bending strength. The goodness of fit for the testing phase was determined as 0.994, 0.986, and 0.989 in the DT, RF, and GP algorithms, respectively. Moreover, the importance of attributes was determined in the algorithms.

Use of Data Mining Techniques for Process Analysis on Small Databases

2010 ◽  
pp. 422-436
Author(s):  
Matjaz Gams ◽  
Matej Ozek
Keyword(s):  
Data Mining ◽  
Decision Trees ◽  
Process Analysis ◽  
Weighted Average ◽  
Basic Rule ◽  
Classification Rules ◽  
Use Of Data ◽  
Long Time ◽  
Tree Branch ◽  
Relevant Rule

The pharmaceutical industry was for a long time founded on rigid rules. With the new PAT initiative, control is becoming significantly more flexible. The Food and Drug Administration is even encouraging the industry to use methods like machine learning. The authors designed a new data mining method based on inducing ensemble decision trees from which rules are generated. The first improvement is specialization for process analysis with only a few examples and many attributes. The second innovation is a graphical module interface enabling process operators to test the influence of parameters on the process itself. The first task is creating accurate knowledge on small datasets. The authors start by building many decision trees on the dataset. Next, they subtract only the best subparts of the constructed trees and create rules from those parts. A best tree subpart is in general a tree branch that covers most examples, is as short as possible and has no misclassified examples. Further on, the rules are weighed, regarding the number of examples and parameters included. The class value of the new case is calculated as a weighted average of all relevant rule predictions. With this procedure the authors retain clarity of the model and the ability to efficiently explain the classification result. In this way, overfitting of decision trees and overpruning of the basic rule learners are diminished to a great extent. From the rules, an expert system is designed that helps process operators. Regarding the second task of graphical interface, the authors modified the Orange explanation module so that an operator at each step takes a look at several space planes, defined by two chosen attributes (Demšar et al., 2004). The displayed attributes are the ones that appeared in the classification rules triggered by the new case. The operator can interactively change the current set of process parameters in order to check the improvement of the class value. The task of seeing the influence of combining all the attributes leading to a high quality end product (called design space) is now becoming human comprehensible, it does not demand a highdimensional space vision any more. The method was successfully implemented on data provided by a pharmaceutical company. High classification accuracy was achieved in a readable form thus introducing new comprehensions.

scholarly journals An Intelligent Data Mining System to Detect Healthcare Fraud

2011 ◽  
pp. 75-95
Author(s):  
Giusseppi A. Forgionne ◽  
Aryya Gangopadhyay ◽  
Monica Adya
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Current Practice ◽  
Fraud Detection ◽  
Mining System ◽  
Healthcare Practice ◽  
Data Mining System

The chapter begins with an overview of the types of healthcare fraud. Next, there is a brief discussion of issues with the current fraud detection approaches. The chapter then develops information technology based approaches and illustrates how these technologies can improve current practice. Finally, there is a summary of the major findings and the implications for healthcare practice.

scholarly journals Hospital Construction Cost Affecting Their Lifecycle: An Italian Overview

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 888
Author(s):  
Leopoldo Sdino ◽  
Andrea Brambilla ◽  
Marta Dell’Ovo ◽  
Benedetta Sdino ◽  
Stefano Capolongo
Keyword(s):  
Healthcare Services ◽  
Construction Cost ◽  
Design Stage ◽  
Quality Healthcare ◽  
Healthcare Facilities ◽  
Early Design Stage ◽  
Hospital Facility ◽  
Italian Context ◽  
The Cost ◽  
Cost Components

The need for 24/7 operation, and the increasing requests of high-quality healthcare services contribute to framing healthcare facilities as a complex topic, also due to the changing and challenging environment and huge impact on the community. Due to its complexity, it is difficult to properly estimate the construction cost in a preliminary phase where easy-to-use parameters are often necessary. Therefore, this paper aims to provide an overview of the issue with reference to the Italian context and proposes an estimation framework for analyzing hospital facilities’ construction cost. First, contributions from literature reviews and 14 case studies were analyzed to identify specific cost components. Then, a questionnaire was administered to construction companies and experts in the field to obtain data coming from practical and real cases. The results obtained from all of the contributions are an overview of the construction cost components. Starting from the data collected and analyzed, a preliminary estimation tool is proposed to identify the minimum and maximum variation in the cost when programming the construction of a hospital, starting from the feasibility phase or the early design stage. The framework involves different factors, such as the number of beds, complexity, typology, localization, technology degree and the type of maintenance and management techniques. This study explores the several elements that compose the cost of a hospital facility and highlights future developments including maintenance and management costs during hospital facilities’ lifecycle.

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