Active subgroup mining: a case study in coronary heart disease risk group detection

Gamberger, Dragan; Lavrač, Nada; Krstačić, Goran

doi:10.1016/s0933-3657(03)00034-4

Cited by 68 publications

(42 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Characterizing subgroup patterns by a set of supporting factors has been proposed by Gamberger et al [11,12]. The methods for obtaining the supporting factors and for ranking these can be regarded as being related to correlation-based methods for relevance analysis of attributes and attribute values.…”

Section: Discussionmentioning

confidence: 99%

“…In that sense the subgroup description defining the subgroup objects (cases) stands for itself. Nevertheless, after subgroup patterns have been discovered, methods for subsequent subgroup characterization and analysis can be very useful: Methods for evaluating and browsing a set of subgroup patterns [11,12], can be applied to obtain further important information about the subgroup objects.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A case-based approach for characterization and analysis of subgroup patterns

Atzmueller

Puppe

2007

Appl Intell

View full text Add to dashboard Cite

In general, cases capture knowledge and concrete experiences of specific situations. By exploiting case-based knowledge for characterizing a subgroup pattern, additional information about the subgroup objects can be provided. This paper proposes a case-based approach for characterizing and analyzing subgroup patterns: It presents techniques for retrieving characteristic factors and a set of corresponding cases for the inspection and analysis of a specific subgroup pattern. Then, the set of factors and cases are merged into prototypical cases for presentation to the user. Such an alternative view on the subgroup pattern provides important introspective information on the subgroup objects, that is, the cases covered by the subgroup description: Using drill-down techniques, the user can perform a detailed introspection of a subgroup pattern using prototypical pattern cases. Additionally, these enable a convenient retrieval of interesting (meta-)information associated with the respective subgroup objects.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A case-based approach for characterization and analysis of subgroup patterns

Atzmueller

Puppe

2007

Appl Intell

View full text Add to dashboard Cite

show abstract

“…The existing research from the patient to collect data using several characteristics and the use of different data mining algorithms to achieve high precision side effects cost of the method. Gamberger et al [7] studied that with the aid of information mining model Intelligent Heart Disease Forecasting System (IHDPS) innovation workmanship for example, decision trees, naïve Bayes and neural system. Chaurasia and Pal [8] explained that the death of the history of the largest study shows that heart disease has gradually become the world's number one killer.…”

Section: Review Of Literaturementioning

confidence: 99%

Data Mining Techniques to Construct a Model: Cardiac Diseases

Akhtar¹,

Talib²,

Kanwal³

2018

ijacsa

View full text Add to dashboard Cite

Abstract-Using echocardiography flexible TransthoracicEchocardiography reported data set detecting heart disease by using mining techniques designed prediction model the data set can develop the reliability of analysis of cardiac diseases by echocardiography, using eight iterative and interactive steps consisting Knowledge Discovery in Database (KDD) methodology including from 209 patients with echocardiography to extracting the data important mode of action Transthoracic Echocardiography inspection report. This study used data from Faisalabad Institute of Cardiology study from 2012 to 2015. All models exposed the results of J48 decision tree, naïve bayes classifier and neural network that has extraordinary classification precision and predictive of heart disease cases are generally comparable. However, J48 model predictive classification accuracy shows of 80% based on the true positive rate ratio and performance slightly better. This study shows to predict heart disease cases and People can be used the results of our study to make more consistent diagnosis of cardiac disease and to help them as a support tool for cardiac disease specialists.

show abstract

“…A database with 238 patients representing typical medical practice in CHD diagnosis, collected at the Institute for Cardiovascular Prevention and Rehabilitation, Zagreb, Croatia, was used for subgroup discovery (Gamberger, Lavrač, & Krstačić, 2003). The database is in no respect a good epidemiological CHD database reflecting actual CHD occurrence in a general population, since about 50% of gathered patient records represent CHD patients.…”

Section: Amentioning

confidence: 99%

Decision Support Through Subgroup Discovery: Three Case Studies and the Lessons Learned

et al. 2004

Self Cite

View full text Add to dashboard Cite

Abstract. This paper presents ways to use subgroup discovery to generate actionable knowledge for decision support. Actionable knowledge is explicit symbolic knowledge, typically presented in the form of rules, that allows the decision maker to recognize some important relations and to perform an appropriate action, such as targeting a direct marketing campaign, or planning a population screening campaign aimed at detecting individuals with high disease risk. Different subgroup discovery approaches are outlined, and their advantages over using standard classification rule learning are discussed. Three case studies, a medical and two marketing ones, are used to present the lessons learned in solving problems requiring actionable knowledge generation for decision support.

show abstract

Active subgroup mining: a case study in coronary heart disease risk group detection

Cited by 68 publications

References 35 publications

A case-based approach for characterization and analysis of subgroup patterns

A case-based approach for characterization and analysis of subgroup patterns

Data Mining Techniques to Construct a Model: Cardiac Diseases

Decision Support Through Subgroup Discovery: Three Case Studies and the Lessons Learned

Contact Info

Product

Resources

About