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Research Paper On Data Mining In Healthcare

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    data mining in healthcare

    Case study: how to apply data mining techniques in a healthcare data warehouse
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    ABSTRACT Healthcare provider organizations are faced with a rising number of financial pressures. Both administrators and physicians need help analyzing large numbers of clinical and financial data when making decisions. To assist them, Rush-Presbyterian–St. Luke's

    A semantic model for multimodal data mining in healthcare information systems
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    Abstract. Electronic health records (EHRs) are representative examples of multimodal/ multisource data collections; including measurements, images and free texts. The diversity of such information sources and the increasing amounts of medical data produced by

    Medical natural language understanding as a supporting technology for data mining in healthcare
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    Medicine is one of these complex domains where new knowledge is accumulated at a daily basis, and at an exponential rate. Most of this knowledge resides in textbooks and papers, a big portion of them resulting from studies conducted on data and information accumulated

    Data mining in healthcare: Current Applications and Issues
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    Abstract The successful application of data mining in highly visible fields like e-business, marketing and retail have led to the popularity of its use in knowledge discovery in databases (KDD) in other industries and sectors. Among these sectors that are just

    Realising the knowledge spiral in healthcare: the role of data mining and knowledge management
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    Prediction Clustering Socialization Externalization Internalization Combination Data Mining Page 12. Data Mining as an enabler of the Knowledge Spiral in healthcare EXPLICIT1 TACIT EXPLICIT Patient ID Drug 1 D1, D2 2 S3, D4, D5 3 D3, D1, D2 4 D3, D5, D1 5 D5, D2

    Data mining applications in healthcare sector a study
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    ABSTRACT: In this paper, we have focused to compare a variety of techniques, approaches and different tools and its impact on the healthcare sector. The goal of data mining application is to turn that data are facts, numbers, or text which can be processed by a

    Role of Data Mining Techniques in Healthcare sector in India
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    Abstract: It is an essential to interpret the correct diagnosis of patient with the help of clinical examination and investigations. Computer information based decision support systems can play an important role in accurate diagnosis and cost effective treatment. Data mining is

    Distributed data mining in a ubiquitous healthcare framework
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    Abstract. Ubiquitous Healthcare (u-healthcare) which focuses on automated applications that can provide healthcare to citizens anywhere/anytime using wired and wireless mobile technologies is becoming increasingly important. Ubiquitous healthcare data provides a

    A data mining analysis of healthcare tourism in Singapore
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    This study examines the factors affecting foreign patients 'choice of hospital type and investigates the gap between the patients 'initial expectations and their actual experience of their hospital stay in Singapore. The survey sample comprises 300 foreign patients.

    Using process and data mining techniques to define and improve standardization in ahealthcare workflow environment
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    Executive Summary The goal of this research project is to help healthcare organizations achieving a standardized and high quality care process by using historic information gathered by registering the day-to-day operations with a healthcare information system.

    Framework for Data Mining In Healthcare Information System in Developing Countries: A Case of Tanzania
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    ABSTRACT: Globally the healthcare sector is abundant with data and hence using data mining techniques in this area seems promising. Healthcare sector collects huge amounts of data on a daily basis. Transferring data into secure electronic system of medical health

    Analysis of Current Applications and Issues of Data Mining in Healthcare
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    ABSTRACT- The application of data mining in the fields like e-business, marketing and retail are successful. They are popular by its use in knowledge discovery in databases (KDD) in other industries and sectors. Among these sectors that are just discovering data

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    Abstract In this paper, we have focused to compare a variety of techniques and various approaches and different types of tools and its effect on the healthcare sector. The objective of data mining application is to find out that data are nothing but facts, numbers, or text

    An Approach to Data Mining in Healthcare: Improved K-means Algorithm
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    Abstract:Nowadays, the application of data mining in the healthcare industry is necessary. Data mining brings a set of tools and techniques that can be applied to discover hidden patterns that provide healthcare professionals an additional source of knowledge for

    Guideline of Data Mining Technique in Healthcare Application
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    Abstract:In today's world, healthcare is the most important factor affecting human life. The management of health care database is the most challenging subject of this era. For this the data mining has been used intensively and extensively by many organizations which are

    Data Mining in Healthcare for Heart Diseases
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    ABSTRACT: Data Mining is the area of research which means digging of useful information or knowledge from previous data. There are different techniques used for the data mining. Data mining may used in different fields including Healthcare. Heart or Cardiovascular

    A Study of Application of Data Mining Algorithms In Healthcare Industry
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    Abstract Data mining is a relatively new area of computer science that brings the concept of artificial intelligence, data structures, statistics, and database together. It is a high demand area because many organizations and businesses can benefit from it. There is no doubt

    Data Mining in Healthcare for Diabetes Mellitus
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    Abstract: Disease diagnosis is one of the applications where data mining tools are proving successful results. Diabetes disease is the leading cause of death all over the world in the past years. Several researchers are using statistical data. The availability of huge amounts

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    ABSTRACT Managing privacy and security of healthcare information used to mine data by reviewing their fundamentals, components and principles as well as relevant laws and regulations. It also presents a literature review on technical issues in privacy assurance

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    ABSTRACT Healthcare facilities have at their disposal vast amounts of data. Thorough analysis of available data on a given problem can lead to more efficient decision-making. The challenge is to extract relevant knowledge from this data and act upon it in a timely

    A Semantic Model for Multimodal Data Mining in Healthcare Information Systems
    free download
    Abstract---Electronic health records (EHRs) are representative examples of multimodal/ multisource data collections; including measurements, images and free texts. The diversity of such information sources and the increasing amounts of medical data produced by

    Cloud based Data Mining framework for monitoring Healthcare in rural India
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    Abstract:Advance Information and Communication Technology (ICT) helps in making healthcare at affordable cost and in improving the effectiveness of healthcare services in remote areas. This paper proposed a framework for monitoring the healthcare of rural

    Applications of Data Mining in Healthcare System
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    Abstract: Data mining has been applied with success to different fields of human endeavor, including marketing, banking, customer relationship management, engineering and various areas of science. However, its application to the analysis of medical data has until recently

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    ABSTRACT: XML Based Information Management is an emerging standard which has gain its popularity because of its various features like transfer of data from one system to another without having a common environment between them and also due to elimination of its

    CRISP-MED-DM methodology for data mining application in healthcare
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    Abstract. There is a lack of specific and detailed framework for conducting data mining analysis in medicine. A number of papers addressed the uniqueness of data mining in health care. All of those papers suggested various additional activities to be considered in

    Data mining concepts in healthcare
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    ABSTRACT Data mining has great contributions to the healthcare such as support for effective treatment, healthcare management, customer relation management, fraud and abuse detection and decision making. The common data mining methods used in

    Data Mining Techniques and Tools Used in Healthcare Databases
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    Abstract This study presents an overview of the current research being carried out on different data mining techniques and tools used in diagnosis, prognosis and treatment of various diseases. Data mining is defined as a process of nontrivial extraction of implicit,

    A Study and Analysis of Disease Prediction Techniques in Data Mining for Healthcare
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    Abstract:ata mining is one of the essential areas of research that is more popular in health organization. Data mining plays an effective role for uncovering new trends in healthcare organization which is helpful for all the parties associated with this field. Heart disease is

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    ABSTRACT The amount of raw data stored in corporate databases is exploding. Trillions of bytes of healthcare data is available and needs to be processed and stored. In healthcare data mining is becoming increasingly popular. The use of data mining in medical

    data mining research papers

    Implementing Data Mining in Primary Health Center–A Review
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    ABSTRACT Data mining is the process of selecting, exploring and modeling a large database in order to discover model and pattern that are unknown. Enormous gathered data in Health care Information society are scattered with different archive systems which are not

    SE code optimization using Data Mining Approach
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    ABSTRACT Data mining also holds promises for other software engineering processes, which have to deal with uncertainty and intangible data such as cost estimation, effort estimation and quality. It can also aid in interesting by helping the software engineer to arrive at the

    accuracy can be find when generic feature extraction and problem specific feature extraction is minimal. Hence for data mining applications, the fuzzy C-means
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    ABSTRACT The present research paper investigates the utilization of web-based facilities for communicating corporate information by companies in India. The sample consists 1325 companies in India. The survey present that companies in India had active websites for

    A Logic Based Feature Selection Method for Improving the Accuracy of Data MiningClassification Algorithms
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    M HACIBEYOGLU, A ARSLAN, S KAHRAMANLI ABSTRACT Real life data sets often contain noisy data which makes the subsequent data mining process difficult. The feature selection preprocessing step can be simplified the datasets by eliminating the features that are redundant for classification process, with

    E-Learning Retrieval System Through Advanced Data Mining Clustering Technique
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    ABSTRACT The e-Learning provides the Learners to interact each other as well as with teachers. The new technology virtual learning system (VLE) is used to interact between the student and teacher. The vast improvement in the field of information technique leads to

    A New Approach To Maintain Privacy And Accuracy In Classification Data Mining
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    ABSTRACT Privacy preserving classified data mining is one of the latest technical fields of data mining in recent years. Transforming the original data in order to maintain accuracy if we apply classification mining on original and transformed data is the key point of the privacy

    Parallel Data Mining Revisited. Better, Not Faster
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    Z Akbar, VN Ivanova, MR Berthold ,informatik.uni-konstanz.de ABSTRACT. In this paper we argue that parallel and/or distributed compute resources can be used differently: instead of focusing on speeding up algorithms, we propose to focus on improving accuracy. In a nutshell, the goal is to tune data mining algorithms to produce

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    ABSTRACT Data mining methods have been increasingly popular and widely used in various application areas, especially on electronic commerce data. The amount of data that organizations have accumulated is enormous, and extracting beneficial information is

    Applying data mining to compare predicted and real success of secondary school students
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    ABSTRACT Schools and academic institutions may improve their curricula by checking student s data and their interoperability. Besides that, checking different student attributes may result in acquiring new knowledge for future improvements. The challenge of many

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    The purpose of this Guide to Data Mining and Data Warehousing is to present the practical perspective of these technologies. This guide is intended to be used by the participants of the Application Programming: Data Mining and Data Warehousing course as the

    A Case of Visual and Interactive Data Analysis: Geospatial Redescription Mining?
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    redescription hold. * This is a pre-print version of the original article presented at the ECML PKDD 2012 Workshop on Instant Interactive Data Mining Page 2. Fig. 1. The Siren interactive mining and visualization tool. The panel

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    ABSTRACT Biofilms develop in drinking water distribution systems (DWDSs) as layers of microorganisms bound by a matrix of organic polymers and attached to pipe walls. Biofilm growth within a DWDS could lead to operational problems, generation of bad tastes and

    An Efficient Parallel Approach for Frequent Itemset Mining of Incremental Data
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    MC Bhadane, K Shah, MP Vispute  Frequent Itemset Mining of Incremental Data Mrs. Chetashri Bhadane, Dr. Ketan Shah, Mrs. Prajakta VisputeAbstract ,Frequent itemset mining is the essential step of data mining process. The process of updating database continuously is known as incremental data mining.

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    P GUPTA ,technicaljournals.org ABSTRACT This paper includes the combined study of Business Intelligence (BI), Data Warehouse and Data Mining technologies. BI techniques are generally computer based that provide past, current and future trends of the enterprise. Its applications include activities

    SL-SecureNet: Intelligent Policing Using Data Mining Techniques
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    MAP Chamikara, Y Yapa, SR Kodituwakku ,International Journal of ABSTRACT Many police departments all around the world lack of good and efficient crime recording and analysis systems. The vast geographical diversity and the complexity of crime patterns have made the analyzing and recording of crime data even difficult. According to

    An Analysis of Diabetes Risk Factors Using Data Mining Approach
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    A Sa-ngasoongsong, J Chongwatpol ,lexjansen.com ABSTRACT Preventing the disease of diabetes is an ongoing area of interest to the healthcare community. Although many studies employ several data mining techniques to assess the leading causes of diabetes, only small sets of clinical risk factors are

    HealthCare Decision Support System using Distributed Data Mining based on Ranking
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    PT Kavitha, T Sasipraba ABSTRACT This paper focuses on a web based decision support system which is based on distributed data mining and designed for the healthcare industry. Innumerable Decision Support Systems are prevailing in the market to cater the needs of the various industries

    Data mining of Basque folk music
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    D Conklin 1. Background This project is concerned with the development and application of data mining methods for the study of Basque song collections. Pattern discovery and classification algorithms are used to discover models and rules that relate musical content

    3 Overview of Talks 3.1 Graph visualization methods and data mining: results, evaluation, and future directions
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    Graph visualization and data mining methods have many areas of common interest. In the introductory talk for this session, I will cover some of my recent results on graph visualization applicable to this topic, outline methods of visualization research, and identify some <

    Fusion of production, operation and maintenance data for underground mobile mining equipment
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     It is especially useful in data mining where different types of data of different quality must be integrated.  AI Magazine 1992; 13, pp. 57-70 12. J Han, M Kamber and J Pei,

    The Evolution of Business Intelligence: From Historical Data Mining to Mobile and Location-based Intelligence
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    Z PANIAN ABSTRACT Business Intelligence (BI) is today seen as a basis for intelligent business and enterprise systems development and design. But, from the time of its birth in late eighties until nowadays business intelligence has passed a long way. The primary interest of

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    ABSTRACT MANET has no clear line of defense so, it is accessible to both legitimate network nodes and malicious nodes. Some of the nodes may be selfish, for example, by not forwarding the packets to the destination, thereby saving the battery power. Some others

    Trading Rules in Targeted/Unknown Multiresidue Pesticides Analysis Using UHPLC-Orbitrap MS and Data Mining Technologies
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    P Yang, J Chang, S Thach, C Yang, K Akervik ,apps.thermoscientific.com The purpose of this work was to characterize operational parameters of ultra-high performance liquid chromatography with Thermo Scientific Orbitrap mass spectrometry systems (UHPLC-Orbitrap) to make the best use of the system for qualitative and

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    ABSTRACT This paper discuss the development of an application for both consumers and companies, which would make use of information available on E-commerce websites to classify the data in accordance with the quality of the products available. Electronic

    Applying Advanced Data Mining Methods for Ground-level Ozone Forecasting
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    R Jiamthapthaksin, Z Yang, X Yuan, H Malki ,prtl.uhcl.edu Accurate ozone forecasting is important for warning the public of potential risks associated with heightened ozone levels. This project developed, implemented, and applied datadriven data mining methods. Six probabilistic estimation decision trees, to the ground level ozone

    Data Warehouse and the Deployment of Data Mining Process to Make Decision for Leishmaniasis in Marrakech City
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    H Mejhed, S Boussaa, N el houda Mejhed In the last decade, the epidemiology applied more and more tools to help to make decision. The aim is to translate epidemic data using the modelling concepts of health information systems for the decision-making. The information is a value-increasing necessary to plan

    Data Mining Methods for Medical Diagnosis
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    ML Mueller ,mediatum.ub.tum.de ABSTRACT As in many areas of life also in medical domains, the volume and complexity of data collected and stored is growing at a rapid pace. In the medical field, this includes besides information obtained in clinical studies also secondary data from patient records, images,

    Data Mining using Random Forests to Predict the Presence of Cylinder Bands in Rotogravure Printing
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    ABSTRACT. This paper concerns an effective data mining strategy for the data set of cylinder bands in rotogravure printing using random forests. Among many data mining algorithms, random forests can be a good data mining tool for the data sets that have some bad

    Logistic regression in feature selection in data mining
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    J Padmavathi ABSTRACT Predictive data mining in clinical medicine deals with learning models to predict patients health. The models can be devoted to support clinicians in diagnostic, therapeutic, or monitoring tasks. Data mining methods are usually applied in clinical contexts to

    Survey of Recent Clustering Techniques in Data Mining
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    ABSTRACT Cluster analysis or clustering is the task of assigning a set of objects into groups called clusters. Main task of clustering are explorative data mining, and a common technique for statistical data analysis used in many fields, including machine learning, pattern

    Improved Data mining approach to find Frequent Itemset Using Support count table
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    R Ahirwal, NK Kori, YK Jain ABSTRACT Mining frequent item sets has been widely studied over the last decade. Past research focuses on mining frequent itemsets from static database. In many of the new applications mining time series and data stream is an important task now. Last decade,


    data mining research papers 2012-113

    data mining research papers 2012-116

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