Survey: Data Mining Techniques in Medical Data Field

Now days most of the research area are working on data mining techniques in medical data. Knowledge discovery and data mining have found numerous applications in business and scientific domain. Valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, we briefly examine the potential use of classification based data mining techniques such as Rule based, decision tree, machine learning algorithms like Support Vector Machines, Principle Component Analysis etc., Rough Set Theory and Fuzzy logic. In particular we consider a case study using classification techniques on a medical data set of diabetic patients

Diagnosis of diseases using data mining

Introduction: In the information age, data are the most important asset for health organizations. In the case of using data in useful and optimal manner, they can become financial resources for organization. Data mining is an appropriate method to transform this potential value into strategic information. Data mining means extraction of hidden information, recognition of hidden relationships and patterns, and in general, discovery of useful knowledge at high volume. The objective of this review paper was to evaluate using data mining in diagnoses of diseases. Methods: This research is a review paper conducted based on a structured review of the papers published in Science Direct, Pubmed, Google Scholar, SID, Magiran (between years 2005 and 2015) and books related to using data mining in medical science and using it in diagnose of diseases with related keywords. Results: Nowadays, data mining is used in many medical science studies, including diagnosis of diseases, discovering the hidden patterns in data, and so on. New ideas such as discovery of Knowledge from Discovery and Data Mining Database, which includes data mining techniques, have found more popularity and they has becomedesired research tool for researchers. Researchers can use them to identify patterns and relationshipsamong great number of variables. Using them, researchers have been able to predict theresults obtained from one disease by using information stores available in databases. Several studies have indicated that data mining is used widely in diagnosis of diseases based on types of information (medical images, characteristics of patients, and so on), such as tuberculosis, types of cancers, infectious diseases, and diagnosis of anomalies rarely diagnosed by human (spots and particular points within aye, which is the symptom of onset of blindness resulting from diabetes), determining type of behavior with patients, and predicting the success rate of surgical surgeries, determining the success rate of therapeutic methods in coping with incurable diseases, and so on. Conclusion: One of the most important challenging topics in healthcare is transformation of raw clinical data into meaningful information following continuous generation of great number of data. In current competitive environment, health organizations using technologies such as data mining to improve healthcare quality will achieve success faster. Many of research centers in Iran are faced with large volume of information, which is not analyzed at all or will be time-consuming due to using traditional methods, even in the case of using analysis and converting them to knowledge. In light of using data mining and its implementation, health organizations can transform the data into a powerful and competitive tool and take new steps in preventing, diagnosing, treating, and providing high-quality services for clients.&nbsp

Analyzing Lifestyle and Environmental Factors on Semen Fertility using Association Rule Mining

The data mining has been used to extract hidden knowledge more effectively for analysis of business, academic, agricultural, as well as medical data in contrast to the predefined queries or reports. This paper presents the impacts of lifestyle and environmental factors of a man on the fertility and quality of semen using association rule mining. The association rules have been mined from data collected by a normalized questionnaire from young volunteers and are found to be useful in predicting the quality of semen based on individual’s lifestyle and environmental factors. Keywords: Association rules, Knowledge Discovery, Fertility potential, Rule confidenc

Case based reasoning versus artificial neural networks in medical diagnosis

Embedding Machine Learning technology into Intelligent Diagnosis Systems adds a new potential to such systems and in particular to the imagiology ones. In our work, this is achieved using the data acquired from MEDsys, a computational environment that supports medical diagnosis systems that use an amalgam of knowledge discovery and data mining techniques, which use the potential of an extension to the language of Logic Programming, with the functionalities of a connectionist approach to problem solving using Artificial Neural Networks. One’s goal aims to conceive an alternative method to detect medical pathologies, as an alternative to the one in use in the actual medical diagnostic system; i.e., Case Based Reasoning versus Artificial Neural Networks. A comparative study of these two approaches to machine learning will be presented, taking into account its applicability in MEDsys

Data Mining: A Novel Outlook to Explore Knowledge in Health and Medical Sciences

Today medical and Healthcare industry generate loads of diverse data about patients, disease diagnosis, prognosis, management, hospitals’ resources, electronic patient health records, medical devices and etc. Using the most efficient processing and analyzing method for knowledge extraction is a key point to cost-saving in clinical decision making. Data mining, sometimes called data or knowledge discovery, is the process of analyzing data from different perspectives and summarizing it into useful information. In medicine, this process is distinct from that in other fields, because of heterogeneity and voluminosity of the data. Herein we reviewed some of published articles about application of data mining in several fields in medicine and healthcare

In silico discoveries for biomedical sciences

Text-mining is a challenging field of research initially meant for reading large text collections with a computer. Text-mining is useful in summarizing text, searching for the informative documents, and most important to do knowledge discovery. Knowledge discovery is the main subject of this thesis. The hypothesis that knowledge discovery is possible started with the work done by Swanson. He made, as a first finding, links between Raynaud__s disease and fish oil using intermediate medical terms to relate them to each other. This principle was formalized in the AB- C concept. A and C are not directly related to each other but via an intermediate concept B that needs to be discovered. Tex data can be extended by adding other non textual data such as microarray experiments. Then we are in the field of data-mining. The final goal is to do all kinds of discoveries with computer (in silico) using data sources in order to assist biology research to save time and discover more.NBICUBL - phd migration 201

Knowledge discovery methodology for medical reports

Medical reports contain valuable information, not only for the patient that waits for the results but also the latent knowledge that is possible to extract from them. The recent introduction of standard structured formats like the Digital Imaging and Communications in Medicine Structured Report and the Clinical Document Architecture Health Level Seven provide an efficient generation, distribution, and management mechanism. Also, they provide an intuitive and effective manner of information representation, unlike the traditional plain text format. In this paper we present a knowledge discovery methodology for structured report interchange based on plain text medical reports using YALE, a leading open-source data mining tool and Open-ESB platform that provides conversion, parsing, different protocols and message formats interchange capabilities.Centro de Imagiologia da Trindade (CIT

INTERACTIVE CLINICAL EVENT PATTERN MINING AND VISUALIZATION USING INSURANCE CLAIMS DATA

With exponential growth on a daily basis, there is potentially valuable information hidden in complex electronic medical records (EMR) systems. In this thesis, several efficient data mining algorithms were explored to discover hidden knowledge in insurance claims data. The first aim was to cluster three levels of information overload(IO) groups among chronic rheumatic disease (CRD) patient groups based on their clinical events extracted from insurance claims data. The second aim was to discover hidden patterns using three renowned pattern mining algorithms: Apriori, frequent pattern growth(FP-Growth), and sequential pattern discovery using equivalence classes(SPADE). The SPADE algorithm was found to be the most efficient method for the dataset used. Finally, a prototype system named myDietPHIL was developed to manage clinical events for CRD patients’ and visualize the relationships of frequent clinical events. The system has been tested and visualization of relationships could facilitate patient education

Prediction of future hospital admissions - what is the tradeoff between specificity and accuracy?

Large amounts of electronic medical records collected by hospitals across the developed world offer unprecedented possibilities for knowledge discovery using computer based data mining and machine learning. Notwithstanding significant research efforts, the use of this data in the prediction of disease development has largely been disappointing. In this paper we examine in detail a recently proposed method which has in preliminary experiments demonstrated highly promising results on real-world data. We scrutinize the authors' claims that the proposed model is scalable and investigate whether the tradeoff between prediction specificity (i.e. the ability of the model to predict a wide number of different ailments) and accuracy (i.e. the ability of the model to make the correct prediction) is practically viable. Our experiments conducted on a data corpus of nearly 3,000,000 admissions support the authors' expectations and demonstrate that the high prediction accuracy is maintained well even when the number of admission types explicitly included in the model is increased to account for 98% of all admissions in the corpus. Thus several promising directions for future work are highlighted.Comment: In Proc. International Conference on Bioinformatics and Computational Biology, April 201