Combining Induction Methods with the Multimethod Approach

Lenic, Mitja; Kokol, Peter; Povalej, Petra; Zorman, Milan

doi:10.4018/9781591405573.ch036

Cited by 4 publications

(6 citation statements)

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“…The multimethod approach 21 introduces the idea of a population of different intelligent systems, i.e. individuals that can produce multiple comparable good solutions, which are incrementally improved using the evolutionary approach.…”

Section: Multimethods Approachmentioning

confidence: 99%

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Intelligent Data Analysis for the Diagnosis of Alcohol Dependence Syndrome

2011

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Alcohol dependence syndrome is hard to diagnose because none of the existing laboratory markers alone has sufficient specificity and sensitivity. This study investigated whether combinations of markers would improve the identification of patients with alcohol dependence syndrome. Intelligent data analysis was carried out using the decision tree induction method with training and test data from 244 healthy volunteers and 238 patients with alcohol dependence syndrome. The results showed that a combination of two or three laboratory markers can identify alcohol dependence syndrome with almost 85% accuracy. It must be noted that induced decision trees offer a qualitatively different diagnostic evaluation of laboratory findings that varies from common practice, because they set up their own new borders and criteria that are different to generally accepted or set reference values. Tests for all of the selected laboratory markers are widely available, inexpensive to perform and usually form part of a routine laboratory examination.

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Section: Multimethods Approachmentioning

confidence: 99%

“…An advanced multimethod approach was used to induce decision trees that can transform extracted knowledge into a humanly understandable form. 21 …”

Section: Introductionmentioning

confidence: 99%

Intelligent Data Analysis for the Diagnosis of Alcohol Dependence Syndrome

2011

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“…The novelty among used approaches is a multimethod approach, which in general produces a population of qualitative solutions represented with hybrids of different methods dynamically combined in non-predefined order [5]. For the reasons exposed above in this research only the methods, which can represent the extracted knowledge in a form of decision trees, are used.…”

Section: Introductionmentioning

confidence: 99%

“…Various methods such as neural networks, decision trees, genetic algorithms, hybrid approaches, etc. have been developed and evaluated on different medical databases [3][4][5][6]. However, from physician's point of view the ability to track and evaluate every step in the decision making process is the most important factor for trusting the decisions gained with machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

Determining risk factors for survival after LMCA stenosis with intelligent data analysis

Povalej

Kanic²,

Kokol

2007

2007 Computers in Cardiology

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“…Main concern of the mutlimethod approach [9] is to find a way to enable dynamic combination of methodologies to the somehow quasi unified knowledge representation. Multiple equally qualitative solutions like in EA approach, where each solution is gained using application of different methodologies with different parameters were used.…”

Section: Multimethods Approachmentioning

confidence: 99%

Multimethod machine learning approach for medical diagnosing

Lenic

Povalej

Zorman

et al.

4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003.

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Abstract-In this paper we present a multimethod approach for induction of a specific class of classifiers, which can assist physicians in medical diagnosing in the case of mitral valve prolapse. Mitral valve prolapse is one of the most controversial prevalent cardiac condition and may affect up to ten percent of the population and in the worst case results in sudden death. MultiVeDec is a general framework enabling researchers to generate various intelligent tools based on machine learning. In this paper we focused on various decision tree methods, which are capable of extracting knowledge in a form closer to human perception, a feature that is very important in medical field. The experiment included classifiers with various classical single method approaches, evolutionary approaches, hybrid approaches and also our newest multimethod approach. The main concern of the latest approach is to find a way to enable dynamic combination of methodologies to the somehow quasi unified knowledge representation. The proposed multimethod approach was capable to outperform all other tested approaches by producing classifier for diagnosing mitral valve prolapse with the highest overall and average class accuracy.Keywordsmitral valve prolapse, multimethod approach. intelligent system, decision making, diagnosing, prevalent cardiac conditions, which may affect from five up to ten percent of adult population, and is also one of the most controversial ones, and thereafter hard to diagnose without the use of expensive technology normally not available to general practitioners.With a selection of knowledge representation and concentrating on decision trees we have only narrowed down the potential set of methods. Thereafter in order to find most appropriate classifier from above we tried out different methods for decision tree induction. First we made analysis with widely known tools for decision tree induction C4.5 and CSiSeeS [2]. Despite acceptable results we wanted to find alternative solutions i.e. evolutionary and multimethod approach.

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Combining Induction Methods with the Multimethod Approach

Cited by 4 publications

References 0 publications

Intelligent Data Analysis for the Diagnosis of Alcohol Dependence Syndrome

Intelligent Data Analysis for the Diagnosis of Alcohol Dependence Syndrome

Determining risk factors for survival after LMCA stenosis with intelligent data analysis

Multimethod machine learning approach for medical diagnosing

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