Type-2 Fuzzy Set and Fuzzy Ontology for Diet Application

Lee, Chang-Shing; Wang, Mei Hui; Hsu, Chin Yuan; Chen, Zhiwei

doi:10.1007/978-1-4614-6666-6_15

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Fuzzy ontology can enhance CBR in different ways such as physician can more easily define experience cases using natural-like language, cases can be indexed more efficiently, and fuzzy-semantic retrieval algorithms can be implemented. Diabetes has utilized fuzzy ontologies in many fields [49]; however, there is no fuzzy ontology-based CBR for diabetes management. CBR effectiveness is further improved if ontologybased CBR systems can utilize vague or imprecise knowledge [21].…”

Section: Regarding the Role Of Fuzzy Ontology In Cbrmentioning

confidence: 99%

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis

El–Sappagh

Elmogy

Riad

2015

Artificial Intelligence in Medicine

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Section: Regarding the Role Of Fuzzy Ontology In Cbrmentioning

confidence: 99%

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis

El–Sappagh

Elmogy

Riad

2015

Artificial Intelligence in Medicine

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“…On the other hand, only a few attempts to deal with the vagueness of medical terms have been made. We recall here the pioneering work to represent medical terms [ 10 ], the fuzzy version of the Arden markup language [ 11 ] and several fuzzy ontology applications to medicine [ 12 , 13 ]. More related to our work is the paper [ 14 ], as discussed later in this introduction.…”

Section: Introductionmentioning

confidence: 99%

Ordinal labels in machine learning: a user-centered approach to improve data validity in medical settings

Seveso

Campagner

Ciucci

et al. 2020

BMC Med Inform Decis Mak

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Background: Despite the vagueness and uncertainty that is intrinsic in any medical act, interpretation and decision (including acts of data reporting and representation of relevant medical conditions), still little research has focused on how to explicitly take this uncertainty into account. In this paper, we focus on the representation of a general and widespread medical terminology, which is grounded on a traditional and well-established convention, to represent severity of health conditions (for instance, pain, visible signs), ranging from Absent to Extreme. Specifically, we will study how both potential patients and doctors perceive the different levels of the terminology in both quantitative and qualitative terms, and if the embedded user knowledge could improve the representation of ordinal values in the construction of machine learning models. Methods: To this aim, we conducted a questionnaire-based research study involving a relatively large sample of 1,152 potential patients and 31 clinicians to represent numerically the perceived meaning of standard and widely-applied labels to describe health conditions. Using these collected values, we then present and discuss different possible fuzzyset based representations that address the vagueness of medical interpretation by taking into account the perceptions of domain experts. We also apply the findings of this user study to evaluate the impact of different encodings on the predictive performance of common machine learning models in regard to a real-world medical prognostic task. Results: We found significant differences in the perception of pain levels between the two user groups. We also show that the proposed encodings can improve the performances of specific classes of models, and discuss when this is the case. Conclusions: In perspective, our hope is that the proposed techniques for ordinal scale representation and ordinal encoding may be useful to the research community, and also that our methodology will be applied to other widely used ordinal scales for improving validity of datasets and bettering the results of machine learning tasks.

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“…Main method used in the diet assessment systems is Fuzzy Logic and its derivatives. Lee et al [10,11] produced Type-2 Fuzzy Ontology based on Type-2 Fuzzy Food Ontology and Fuzzy Markup Languege-2(FML2). Similarly, they presents to the user healthy level of the foods in diet with semantic expressions by developing FML2 based agent.…”

Section: Introductionmentioning

confidence: 99%

Preparing Diet List Suggestion with Fuzzy Expert System

Uyar

2016

ijisae

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Proportion of disease is growing due to the malnutrition and sedentary life. In this work, a diet list is proposed to the user by calculating the necessary amount of calorie according to gender, weight, height, age and activity level. Diet list are prepared for seven days of a week as divided by six meals. Parameter assessment of users and offering recommendations are made via fuzzy expert system. Prepared diet list are constituted considering calories of nutrients and based on the dieticians' general diet list proposals. Developed software also includes some functions such as nutrition advices, calculation of ideal weight, information about benefits of several nutrients and calorie evaluation of some daily activities. Thus, nutrition suggestion software carried out against for growing obesity and healthy eating problems in order that people would educate themselves about wellness.

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Type-2 Fuzzy Set and Fuzzy Ontology for Diet Application

Cited by 5 publications

References 15 publications

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis

Ordinal labels in machine learning: a user-centered approach to improve data validity in medical settings

Preparing Diet List Suggestion with Fuzzy Expert System

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