Data Mining - Concepts and Techniques.

Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

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 „knowledge poor‟. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. This research paper intends to provide a survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today‟s medical research particularly in Heart Disease Prediction. Number of experiment has been conducted to compare the performance of predictive data mining technique on the same dataset and the outcome reveals that Decision Tree outperforms and some time Bayesian classification is having similar accuracy as of decision tree but other predictive methods like KNN, Neural Networks, Classification based on clustering are not performing well. The second conclusion is that the accuracy of the Decision Tree and Bayesian Classification further improves after applying genetic algorithm to reduce the actual data size to get the optimal subset of attribute sufficient for heart disease prediction.

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Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction

The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not ";mined"; to discover hidden information for effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining techniques can help remedy this situation. This research has developed a prototype Intelligent Heart Disease Prediction System (IHDPS) using data mining techniques, namely, Decision Trees, Naive Bayes and Neural Network. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. IHDPS can answer complex ";what if"; queries which traditional decision support systems cannot. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established. IHDPS is Web-based, user-friendly, scalable, reliable and expandable. It is implemented on the .NET platform.

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Intelligent heart disease prediction system using data mining techniques

In order to determine how data mining techniques (DMT) and their applications have developed, during the past decade, this paper reviews data mining techniques and their applications and development, through a survey of literature and the classification of articles, from 2000 to 2011. Keyword indices and article abstracts were used to identify 216 articles concerning DMT applications, from 159 academic journals (retrieved from five online databases), this paper surveys and classifies DMT, with respect to the following three areas: knowledge types, analysis types, and architecture types, together with their applications in different research and practical domains. A discussion deals with the direction of any future developments in DMT methodologies and applications: (1) DMT is finding increasing applications in expertise orientation and the development of applications for DMT is a problem-oriented domain. (2) It is suggested that different social science methodologies, such as psychology, cognitive science and human behavior might implement DMT, as an alternative to the methodologies already on offer. (3) The ability to continually change and acquire new understanding is a driving force for the application of DMT and this will allow many new future applications.

https://romisatriawahono.net/lecture/rm/survey/machine%20learning/Liao%20-%20Data%20mining%20techniques%20and%20applications%20A%20decade%20review%20from%202000%20to%202011%20-%202012.pdf

Review: Data mining techniques and applications - A decade review from 2000 to 2011

The healthcare environment is generally perceived as being ‘information rich’ yet ‘knowledge poor’. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in 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 and Artificial Neural Network to massive volume of healthcare data. In particular we consider a case study using classification techniques on a medical data set of diabetic patients.

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Empirical Study on Applications of Data Mining Techniques in Healthcare

Medical data mining has great potential for exploring the hidden patterns in the data sets of the medical domain. These patterns can be utilized for clinical diagnosis. However, the available raw medical data are widely distributed, heterogeneous in nature, and voluminous. These data need to be collected in an organized form. This collected data can be then integrated to form a hospital information system. Data mining technology provides a user-oriented approach to novel and hidden patterns in the data. Data mining and statistics both strive towards discovering patterns and structures in data. Statistics deals with heterogeneous numbers only, whereas data mining deals with heterogeneous fields. We identify a few areas of healthcare where these techniques can be applied to healthcare databases for knowledge discovery. In this paper we briefly examine the impact of data mining techniques, including artificial neural networks, on medical diagnostics.

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The impact of data mining techniques on medical diagnostics

Quasi-cyclic (QC) codes are a remarkable generalization of cyclic codes. Many QC codes have been shown to be best for their parameters. In this paper, some structural properties of QC codes over the prime power integer residue ring $${\mathbb{Z}_q}$$are considered. An l-QC code of length lm over $${\mathbb{Z}_q}$$is viewed both as in the conventional row circulant form and also as a $${\frac{\mathbb{Z}_q[x]}{\langle x^m-1 \rangle}}$$-submodule of $${\frac{GR(q,l)[x]}{\langle x^m-1 \rangle}}$$, where GR(q, l) is the Galois extension ring of degree l over $${\mathbb{Z}_q}$$. A necessary and sufficient condition for cyclic codes over Galois rings to be free is obtained and a BCH type bound for them is also given. A sufficient condition for 1-generator QC codes to be $${\mathbb{Z}_q}$$-free is given and a formula to evaluate their ranks is derived. Some distance bounds for 1-generator QC codes are also discussed. The duals of QC codes over $${\mathbb{Z}_q}$$are also briefly discussed.

On quasi-cyclic codes over $${\mathbb{Z}_q}$$

In this paper, we propose I-MIN model for knowledge discovery and knowledge management in evolving databases. The model splits the KDD process into three phases. The schema designed during the first phase, abstracts the generic mining requirements of the KDD process and provides a mapping between the generic KDD process and (user) specific KDD subprocesses. The generic process is executed periodically during the second phase and windows of condensed knowledge called knowledge concentrates are created. During the third phase, which corresponds to actual mining by the end users, specific KDD subprocesses are invoked to mine knowledge concentrates. The model provides a set of mining operators for the development of mining applications to discover and renew, preserve and reuse, and share knowledge for effective knowledge management. These operators can be invoked by either using a declarative query language or by writing applications.The architectural proposal emulates a DBMS like environment for the managers, administrators and end users in the organization. Knowledge management functions, like sharing and reuse of the discovered knowledge among the users and periodic updating of the discovered knowledge are supported. Complete documentation and control of all the KDD endeavors in an organization are facilitated by the I-MIN model. This helps in structuring and streamlining the KDD operations in an organization.

Architecture for knowledge discovery and knowledge management

Deriving information from mountain of heathcare data can be too complex and voluminous to be processed by human capabilities alone. To overcome this flaw or nature of data, healthcare practioners are adopting new emergent technology to discover effective and efficient pattterns. However, medical researchers are exploiting numerous data mining techniques to find correlations or patterns among large scale databases as compared to traditional techniques for future medical diagnosis. Data mining can be termed as an essential component for knowledge discovery process, where the vivid process determines effective and potential benefits among the data. However challenge is to establish proper exploitation strategy among health care data records which offer numerous facts in creation, dissemination and preservation of knowledge using advanced technologies, whereas, if the discovered knowledge tends to be a successful activity then gradually it can be used for futuristic decision making in healthcare organization. For instance if data of cancer patients or other diseases might consists of knowledgeable patterns which can be more expected to develop a kind of disease, so such knowledge can be used to prevent the diagnosis of patient’s disease for futuristic decision making. Progressively Data mining and knowledge discovery are used as interdisciplinary terms to discover hidden and unknown information from large scale databases. Eventually data mining is also known as essential step in knowledge discovery process, knowledge discovery process includes several integrated preprocessing and post processing steps to discover hidden information from databases. There exists several application domain areas of data mining techniques such as medical domain for diagnosis, management survey of data, marketing area of research, statistical analysis, and geographical analysis of data and several other research areas (Arabie & Hubert, 1994; Dunham, 2003; Kaur et al., 2010; Chauhan & Kaur, 2014). Data mining techniques are highly computational techniques under certain computational circumstances to retrieve effective and efficient patterns from raw data (Fayyad et al., 1996). The output of data mining techniques can be further applied for decision making support system, to retrieve profitable environment for experts and finally provide benefits to end users. Such analyses are increasing pressure on healthcare organizations to make decision based on data mining techniques to gain insights of data. Data mining can influence medical decision making by maintaining high level of healthcare. Medical decision making from diagnosis to patient management is becoming more and more complex due to rapid growth of knowledge during last three decades. It is possible that even with specialization and super specialization physician may not be able to make an optimal decision. Computer assisted Medical Decision making use of data mining techniques may provide a partial solution to the problem. Since medical diagnosis is probabilistic in nature, it is well suited for probabilistic formalism. Bayesian classifiers are statistical classifiers based on famous Bayes theorem of conditional probability. Thus medical diagnosis fits well into Bayesian probabilistic framework. But there Harleen Kaur Hamdard University, India

Siri Krishan Wasan

Papers

Empirical Study on Applications of Data Mining Techniques in Healthcare

The impact of data mining techniques on medical diagnostics

On quasi-cyclic codes over $${\mathbb{Z}_q}$$

Architecture for knowledge discovery and knowledge management

A Bayesian Network Model for Probability Estimation