Fuzzy decision trees in the support of breastfeeding

Babic, S.H.; Kokol, Peter; Štiglic, Milojka Molan

doi:10.1109/cbms.2000.856864

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…Therefore, introducing the fuzzy sets and logic, and certainty factors into the decision trees is necessary. Babie, et al [1] developed a tool for building fuzzy decision trees from the training set of data collected in the evaluation of breastfeeding. Certainty Factors were first suggested by Shortliffe and Buchanan in their famous medical diagnostic system MYCIN.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Traditional Chinese medical diagnosis based on fuzzy and certainty reasoning

Shieh

Yang

2008

2008 IEEE International Symposium on IT in Medicine and Education

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Diagnosis in Traditional Chinese Medicine (TCM) is the foundation of every TCM clinical branch. It is related to basic TCM theories, and guides TCM treatments.In this paper methodologies developed for quick and reliable TCM diagnosis are described. The diagnosis process is divided into two stages. In the first stage the scope of possible diseases and syndromes of a patient will be quickly narrowed down by a modifiable multilevel fuzzy sieving method. This sieving method follows TCM differentiation principles. In the second stage diseases and syndromes will be reliably determined by a multi-agent diagnosis helping system (MADHS) that simulates cooperated experts and makes joint decisions. Fuzziness and uncertainty are incorporated into decision trees to form the reasoning mechanism of agents. A prototype of MADHS has been implemented and successfully tested.It is anticipated that the system and methodologies may be widely used in computer aided diagnosis and TCM education software.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Traditional Chinese medical diagnosis based on fuzzy and certainty reasoning

Shieh

Yang

2008

2008 IEEE International Symposium on IT in Medicine and Education

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“…To offer widespread use and availability and enable verification of results Cremilleux and Robert28 offered a general framework for using DTs for medical application. Decision trees have been applied in many areas of medicine: Tsien et al29 showed the competitiveness of decision trees for diagnosing myocardial infarction, and Babic et al30 used fuzzy decision trees in support of breastfeeding, etc. Despite the wide area where DTs have been applied, classical approaches to their induction are not always optimal.…”

Section: Decision Treesmentioning

confidence: 99%

Evolutionary design of decision trees for medical application

Kokol

Pohorec

Štiglic

et al. 2012

WIREs Data Min & Knowl

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Decision trees (DT) are a type of data classifiers. A typical classifier works in two phases. In the first, the learning phase, the classifier is built according to a preexisting data (training) set. Because decision trees are being induced from a known training set, and the labels on each example are known the first step can also be referred to as supervised learning. The second step is when the induced classifier is used for classification. Usually, prior to the first step several steps should be performed to improve the accuracy and efficiency of the classification: data cleaning, redundancy elimination, and data normalization. Classifiers are evaluated for accuracy, speed, robustness, scalability, and interpretability. DTs are widely used for exploratory knowledge discovery where comprehensible knowledge representation is preferred. The main attraction of DTs lies in the intuitive representation that is easy to understand and comprehend. Accuracy, however, is dependent on the learning data. One of the methods to improve the induction and other phases in the creation of a classifier is the use of evolutionary algorithms. They are used because the classic deterministic approach is not necessarily optimal with regard to the quality, accuracy, and complexity of the obtained classifier. In addition to the description of different evolutionary DT induction approaches, this paper also presents multiple examples of evolutionary DT applications in the medical domain. © 2012 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Hierarchies and Trees Technologies > Classification

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“…al. [1] developed a tool for building fuzzy decision trees from the training set of data collected in the evaluation of breastfeeding. Certainty factors were first suggested by Shortliffe and Buchanan in their famous medical diagnostic system MYCIN.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Network supported intelligent cooperative diagnosis for difficult and complicated cases in Traditional Chinese Medicine

Shieh

2011

The Fourth International Workshop on Advanced Computational Intelligence

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The diagnosis of difficult and complicated cases inTraditional Chinese Medicine (TCM) often requires experts to cooperatively work together. The right experts and their ability to jointly make the suitable diagnostic decisions are key for resolving the diagnosis.The methodologies described here have been developed for (1) effectively making an initial diagnosis or narrowing down the possible syndrome and disease range, (2) forming a cooperative team via networks, and (3) making a joint decision among multi-agents. The process is divided into two stages. In the first stage, the doctor in charge uses a multi-level sieve method to narrow down the range of possible diseases and syndromes. Important symptoms and diagnostic difficulties will be identified in the sieving process. In the second stage, a multi-agent diagnosis helping system (MADHS) simulates cooperative experts and makes joint decisions. Calls for invitations are distributed by a Network Supported Invitation System (NSIS) when 1) the sieving method is not able to narrow down the range of possible diseases and syndromes, or 2) using the certainty reasoning method is not able to confirm or deny certain diseases or syndromes. Invited experts may reply by sending their intentions and proposals to join the team. Received proposals from experts a team of experts will be collated by the intelligent NSIS. Diseases and syndromes will be further reliably determined by the MADHS system. It is anticipated that the methodologies used here may be widely implemented in computer aided diagnosis not only for TCM, but also for other diagnostic areas. The described system may be implemented as a cloud service system for many expected users.

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Fuzzy decision trees in the support of breastfeeding

Cited by 9 publications

References 4 publications

Traditional Chinese medical diagnosis based on fuzzy and certainty reasoning

Traditional Chinese medical diagnosis based on fuzzy and certainty reasoning

Evolutionary design of decision trees for medical application

Network supported intelligent cooperative diagnosis for difficult and complicated cases in Traditional Chinese Medicine

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