Mining constrained association rules to predict heart disease

Ordóñez, Carlos; Omiecinski, Edward; Braal, L. de; Santana, Cesar A.; Ezquerra, Norberto; Taboada, José Antonio Franco; Cooke, Denise E.; Krawczynska, Elizabeth; Garcia, Ernest

doi:10.1109/icdm.2001.989549

Cited by 77 publications

(52 citation statements)

References 9 publications

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“…Most of the research so far has focussed on developing efficient, sound and complete evaluation strategies for constraintbased mining queries, and regardless some successful applications, e.g., in medical domain [6][7][8], or in biological domain [9], there is still a lack of research on languages and systems supporting this knowledge discovery paradigm. Indeed, to the best of our knowledge, CONQUEST is the first and only system of this kind.…”

Section: Introductionmentioning

confidence: 99%

A constraint-based querying system for exploratory pattern discovery

Bonchi

Giannotti

Lucchese

et al. 2009

Information Systems

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a b s t r a c tIn this article we present CONQUEST, a constraint-based querying system able to support the intrinsically exploratory (i.e., human-guided, interactive and iterative) nature of pattern discovery. Following the inductive database vision, our framework provides users with an expressive constraint-based query language, which allows the discovery process to be effectively driven toward potentially interesting patterns. Such constraints are also exploited to reduce the cost of pattern mining computation. CONQUEST is a comprehensive mining system that can access real-world relational databases from which to extract data. Through the interaction with a friendly graphical user interface (GUI), the user can define complex mining queries by means of few clicks. After a pre-processing step, mining queries are answered by an efficient and robust pattern mining engine which entails the state-of-the-art of data and search space reduction techniques. Resulting patterns are then presented to the user in a pattern browsing window, and possibly stored back in the underlying database as relations.

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Section: Introductionmentioning

confidence: 99%

A constraint-based querying system for exploratory pattern discovery

Bonchi

Giannotti

Lucchese

et al. 2009

Information Systems

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“…We must however learn from history and ensure that the validation requirements for knowledge acquisition, as discussed previously, are adhered to by any automated process as for all other methods of knowledge acquisition even though this has been described as "the hardest part of the expert system development task" (Lavrak et al, 2000). Too often in recent work there has been a focus on developing new methods for determining the quality and value of outputs which does not take into consideration the many lessons we can learn from history, and is often little more than a process of reinventing an already rolling wheel (Ordonez et al, 2001). …”

Section: The Application Of Data Miningmentioning

confidence: 99%

“…• The complexity of medical data frequently results in a huge number of results; too many to be evaluated by the human user and a method for reducing the results to only those of greatest interest is necessary (Roddick et al, 2003;Ordonez et al, 2001) • Each user has a trusted set of methods which are applied during clinical analysis and which are rarely seen in a system that is not purpose built for that user or their analytical requirements; this increases the cost of providing the technology, the frustration in trying to use the technology and allows the technology to dictate the analytical process rather than the other way around. Being able to facilitate and apply a range of interest definitions in a single system would open up the technology to a greater audience.…”

Section: Data Mining In Medical and Biological Research 80mentioning

confidence: 99%

Building Clinical Trust in Automated Knowledge Acquisition

Shillabeer¹

2008

Data Mining in Medical and Biological Research

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The aim of this chapter is to describe the process of medical knowledge acquisition from a historical context and to define the requirements for producing knowledge which is able to be trusted and applied in a clinical setting. This is related to modern data mining approaches which do not yet adequately address these requirements. This is believed to be the most critical issue in the acceptance of data mining in the medical domain. The chapter will discuss how data mining can address these needs and will provide discussion of a technical solution to the stated issues. Overall this chapter aims to demonstrate that the individual needs of all medical professionals can be addressed and that data mining can be a valuable tool in the diagnostic and decision making toolkit. It will also empower medical professionals to take a greater role in the development of such systems by providing a checklist of features to include and pitfalls to avoid, thus ensuring greater success in future systems. Introduction Clinical data mining contextWhile acceptance of data mining technologies is growing, progress has been slow and it is not yet an integrated part of the medical data analysis toolkit. Many reasons have been documented for this but the primary issues are two fold; the decision making and knowledge acquisition processes of the medical domain are not adequately reflected in the technologies available and the systems are too often built to suit the specific analytical needs of an individual user. Whilst this has enabled the application of the technology in specific scenarios, it has resulted in the development of tools which cannot be utilised outside of the specific purpose for which they were built. These issues serve to limit the exposure, applicability and trust of data mining systems to the medical domain. Data mining researchers have long been concerned with the application of tools to facilitate and improve data analysis on large, complex data sets for the purpose of knowledge acquisition. The current challenge is to make data mining and knowledge discovery systems applicable to a wider range of domains, among them medicine. Early work was performed over transactional, retail based data sets, but the attraction of finding previously unknown knowledge from the increasing volume of data collected from the medical domain is an emerging area of interest and specialisation. This chapter is primarily concerned with Open Access Database www.intechweb.org There is a growing body of work which aims to qualify and define a process for the discovery of new knowledge accross a range of domains, however this work has not focused on the unique needs of the medical domain and hence the domain has remained relatively untouched by the advances in data mining and knowledge discovery technology. The primary challenge presented by medicine is to develop a technology that can apply a trusted knowledge acquisition process to reveal data patterns in the form of hypotheses which are based on measures that can be relied upon in medical healt...

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“…Ordonez, et al [14] The contribution of their paper was to find and discover new association rules in medical data to predict heart disease and validating rules used by an expert system to aid in diagnosing coronary heart disease. The authors of this paper focused on two aspects in this work.…”

Section: Association Rules In Medical Domainmentioning

confidence: 99%

Template based Medical Reports Summarization

El-Qumsan¹,

El-Halees²

2018

IJCA

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The torrential information in the medical records is considered a great problem because it difficult to distinguish the needed and necessary information from the huge quantity of data. As a result, the importance of summarize medical reports is growing day after day. Medical information extraction is one of the important topics that aim to identify medical information and detect hidden relations. This topic is considered one of the most important topics in the field of text mining where is used to process unstructured texts and extract meaningful information which is hidden in the unstructured texts.The information extracted from medical reports is very useful to medical staff to detect hidden relations between medical information, and making decisions that will improve the medical service for patients, in addition to saving time and effort.In our paper, an approach that use template based medical reports summarization has been developed to transfer medical reports from semi structured and unstructured form to structured form. It classifies the identified entities then extracts important information such as diseases, medical procedures, and drugs. After that, it can discovery hidden relationship between medical information by using association rules. The dataset used in this paper was collected from the Palestinian Ministry of Health.To evaluate the performance and effectiveness of our model, human expert has been used as a reference to measure the degree of acceptance of the extracted association rules which have been extracted from the dataset. So, Likert's scale has been used for evaluation. After the data analysis obtained from the questionnaire. It shows us that the proportion of accuracy association rules, which have been extracted is about 80%.

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Mining constrained association rules to predict heart disease

Cited by 77 publications

References 9 publications

A constraint-based querying system for exploratory pattern discovery

A constraint-based querying system for exploratory pattern discovery

Building Clinical Trust in Automated Knowledge Acquisition

Template based Medical Reports Summarization

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