medical data mining


Data mining is the process of pattern discovery and extraction where huge amount of data is involved. Both the data mining and healthcare industry have emerged some of reliable early detection systems and other various healthcare related systems from the clinical and diagnosis data.

Predictive data mining for medical diagnosis: An overview of heart disease prediction
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The successful application of data mining in highly visible fields like e-business, marketing and retail has led to its application in other industries and sectors. Among these sectors just discovering is healthcare. The healthcare environment is still information rich‟ but

ROC-based evolutionary learning: Application to medical data mining
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A novel way of comparing supervised learning algorithms has been introduced since the late 90s, based on Receiver Operating Characteristics (ROC) curves. From this approach is derived a NP complete optimization criterion for supervised learning, the area under the

Medical data mining based on association rules
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Detailed elaborations are presented for the idea on two-step frequent itemsets Apriori algorithm of Association Rules. An improved method called Improved Apriori algorithm is brought forward owing to the disadvantages of Apriori algorithm. Moreover, based on

A survey of data mining techniques on medical data for finding locally frequent diseases
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In the last decade there has been increasing usage of data mining techniques on medical data for discovering useful trends or patterns that are used in diagnosis and decision making. Data mining techniques such as clustering, classification, regression, association

Mining medical data for predictive and sequential patterns: PKDD 2001
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Data relating to patient information and medical exams connected with thrombosis attacks were analysed using SPSS Clementine data mining workbench. The ability to predict the onset and successful diagnosis of thrombosis is key to the intervention of the disease, and

A hybrid data mining method for the medical classification of chest pain
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Data mining techniques have been used in medical research for many years and have been known to be effective. In order to solve such problems as long-waiting time, congestion, and delayed patient care, faced by emergency departments, this study concentrates on building

Intelligent data mining for medical quality management
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In the healthcare sector cost pressure is growing, quality demands are rising and the competitive situation amongst suppliers is mounting. These developments confront hospitals more than ever with the necessities of critically reviewing their own efficiency under both

Incremental SVM and visualization tools for bio- medical data mining
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Most of the bio- data analysis problems process datasets with a very large number of attributes and few training data . This situation is usually suited for support vector machine (SVM) approaches. We have implemented a new column-incremental linear proximal SVM

Efficient Implementation of FP Growth Algorithm- Data Mining on Medical Data
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Data mining plays an essential role for extracting knowledge from large databases from enterprises operational databases. Everyday organizations collect huge amount of data from several resource. Whereas medical data is consider most famous application to mine that

Application of k-nearest neighbour classification in medical data mining
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Medical data is an ever-growing source of information from hospitals in form of patient records. When mined, the information hidden in these records is a huge resource bank for medical research. This data contains hidden patterns and relationships, which can lead to

Data mining : medical and engineering case studies
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In the paper the problem of predicting outcomes in medical and engineering applications is discussed. The problem is solved with a data mining approach. The first of the two algorithms presented in the paper generates a solution that is validated with the second

Feature selection for classification in medical data mining
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Feature selection is an important step in classification and also for dimensionality reduction. As medical information is with multiple attributes, medical data mining differs from other one. Diagnosis of most of the diseases is expensive as many tests are required to predict the

Medical diagnosis data mining based on improved Apriori algorithm
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With the wide application of computer science and technology, the amount of data generated by various disciplines increased rapidly. In order to discover valuable knowledge in these databases, people use data mining methods to solve this problem. The application

Comparative study of different data mining techniques performance in knowledge discovery from medical database
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Medical dataset is a vital ingredient used in predicting patients health condition. In other to have the best prediction, there calls for a technique with high degree of accuracy. In this paper, we modeled data from diabetes patients and used it to predict the diabetes

A Medical Claim Fraud/Abuse Detection System based on Data Mining : A Case Study in Chile.
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This paper describes an effective medical claim fraud/abuse detection system based on data mining used by a Chilean private health insurance company. Fraud and abuse in medical claims have become a major concern within health insurance companies in Chile the last

Identification of outliers in medical diagnostic system using data mining techniques
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The outlier detection problem has important applications in the field of medical research. Clinical databases have accumulated large quantities of information about patients and their medical conditions. In this study, the data mining techniques are used to search for

Medical data mining based on decision tree algorithm
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Detailed elaborations are presented for the idea on ID3 algorithm of Decision Tree. An improved method called Improved ID3 algorithm that can improve the speed of generation is brought forward owing to the disadvantages of ID3 algorithm. Moreover, based on Improved

Data mining in health and medical information
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Data mining (DM) is part of a process by which information or knowledge can be extracted from data or databases and used to inform decision-making in a variety of contexts (Michalski et al., 1997; Benoit). The process of DM includes a range of tools and

CRISP data mining methodology extension for medical domain
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There is a lack of specific and detailed framework for conducting data mining analysis in medicine. Cross Industry Standard Process for Data Mining (CRISP-DM) presents a hierarchical and iterative process model, and provides an extendable framework with

Comparative analysis of data mining tools and classification techniques using weka in medical bioinformatics
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The availability of huge amounts of data resulted in great need of data mining technique in order to generate useful knowledge. In the present study we provide detailed information about data mining techniques with more focus on classification techniques as one important


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