A Fuzzy Approach for Diabetes Mellitus Type 2 Classification
Braz. arch. biol. technol
;
63: e20180742, 2020. tab, graf
Article
Dans Anglais
| LILACS
| ID: biblio-1132274
ABSTRACT
Abstract This paper proposes an automatic fuzzy classification system for glycemic index, which indicates the level of Diabetes Mellitus type 2. Diabetes is a chronic disease occurred when there is deficiency in insulin production or in its action, or both, causing complications. Neuro-fuzzy systems and Decision Trees are used to obtain, respectively, the numerical parameters of the membership functions and the linguistic based rules of the fuzzy classification system. The results goal to categorize the glycemic index into 4 classes decrease a lot, decrease, stable and increase. Real database from [1] is used and the input attributes of the system are defined. In addition, the proposed automatic fuzzy classification system is compared with an "expert" fuzzy classification system, which is totally modeled using expert knowledge. From linguistic based rules obtained from fuzzy inference process, new scenarios are simulated in order to obtain a larger data set which provides a better evaluation of the classification systems. Results are promising, since they indicate the best treatment - intervention or comparative - for each patient, assisting in the decision-making process of the health care professional.
Texte intégral:
Disponible
Indice:
LILAS (Amériques)
Sujet Principal:
Diabète de type 2
Type d'étude:
Étude pronostique
Limites du sujet:
Humains
langue:
Anglais
Texte intégral:
Braz. arch. biol. technol
Thème du journal:
Biologie
Année:
2020
Type:
Article
Pays d'affiliation:
Brésil
Institution/Pays d'affiliation:
Universidade Tecnológica do Paraná/BR
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