Generating fuzzy rules for constructing interpretable classifier of diabetes disease.
Identifieur interne : 000170 ( Main/Exploration ); précédent : 000169; suivant : 000171Generating fuzzy rules for constructing interpretable classifier of diabetes disease.
Auteurs : Nesma Settouti [Algérie] ; M Amine Chikh ; Meryem SaidiSource :
- Australasian physical & engineering sciences in medicine [ 0158-9938 ] ; 2012.
Descripteurs français
- KwdFr :
- Algorithmes (MeSH), Diabète (classification), Diabète (diagnostic), Diagnostic assisté par ordinateur (méthodes), Humains (MeSH), Logique floue (MeSH), Reconnaissance automatique des formes (méthodes), Reproductibilité des résultats (MeSH), Sensibilité et spécificité (MeSH), Systèmes d'aide à la décision clinique (MeSH).
- MESH :
- diagnostic : Diabète.
- méthodes : Diagnostic assisté par ordinateur, Reconnaissance automatique des formes.
- classification : Algorithmes, Diabète, Humains, Logique floue, Reproductibilité des résultats, Sensibilité et spécificité, Systèmes d'aide à la décision clinique.
English descriptors
- KwdEn :
- Algorithms (MeSH), Decision Support Systems, Clinical (MeSH), Diabetes Mellitus (classification), Diabetes Mellitus (diagnosis), Diagnosis, Computer-Assisted (methods), Fuzzy Logic (MeSH), Humans (MeSH), Pattern Recognition, Automated (methods), Reproducibility of Results (MeSH), Sensitivity and Specificity (MeSH).
- MESH :
- classification : Diabetes Mellitus.
- diagnosis : Diabetes Mellitus.
- methods : Diagnosis, Computer-Assisted, Pattern Recognition, Automated.
- Algorithms, Decision Support Systems, Clinical, Fuzzy Logic, Humans, Reproducibility of Results, Sensitivity and Specificity.
Abstract
Diabetes is a type of disease in which the body fails to regulate the amount of glucose necessary for the body. It does not allow the body to produce or properly use insulin. Diabetes has widespread fallout, with a large people affected by it in world. In this paper; we demonstrate that a fuzzy c-means-neuro-fuzzy rule-based classifier of diabetes disease with an acceptable interpretability is obtained. The accuracy of the classifier is measured by the number of correctly recognized diabetes record while its complexity is measured by the number of fuzzy rules extracted. Experimental results show that the proposed fuzzy classifier can achieve a good tradeoff between the accuracy and interpretability. Also the basic structure of the fuzzy rules which were automatically extracted from the UCI Machine learning database shows strong similarities to the rules applied by human experts. Results are compared to other approaches in the literature. The proposed approach gives more compact, interpretable and accurate classifier.
DOI: 10.1007/s13246-012-0155-z
PubMed: 22895813
Affiliations:
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Le document en format XML
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(MeSH)</term><term>Systèmes d'aide à la décision clinique (MeSH)</term></keywords><keywords scheme="MESH" qualifier="classification" xml:lang="en"><term>Diabetes Mellitus</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Diabetes Mellitus</term></keywords><keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Diabète</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Diagnosis, Computer-Assisted</term><term>Pattern Recognition, Automated</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Diagnostic assisté par ordinateur</term><term>Reconnaissance automatique des formes</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Decision Support Systems, Clinical</term><term>Fuzzy Logic</term><term>Humans</term><term>Reproducibility of Results</term><term>Sensitivity and Specificity</term></keywords><keywords scheme="MESH" qualifier="classification" xml:lang="fr"><term>Algorithmes</term><term>Diabète</term><term>Humains</term><term>Logique floue</term><term>Reproductibilité des résultats</term><term>Sensibilité et spécificité</term><term>Systèmes d'aide à la décision clinique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Diabetes is a type of disease in which the body fails to regulate the amount of glucose necessary for the body. It does not allow the body to produce or properly use insulin. Diabetes has widespread fallout, with a large people affected by it in world. In this paper; we demonstrate that a fuzzy c-means-neuro-fuzzy rule-based classifier of diabetes disease with an acceptable interpretability is obtained. The accuracy of the classifier is measured by the number of correctly recognized diabetes record while its complexity is measured by the number of fuzzy rules extracted. Experimental results show that the proposed fuzzy classifier can achieve a good tradeoff between the accuracy and interpretability. Also the basic structure of the fuzzy rules which were automatically extracted from the UCI Machine learning database shows strong similarities to the rules applied by human experts. Results are compared to other approaches in the literature. The proposed approach gives more compact, interpretable and accurate classifier.</div></front></TEI><affiliations><list><country><li>Algérie</li></country></list><tree><noCountry><name sortKey="Chikh, M Amine" sort="Chikh, M Amine" uniqKey="Chikh M" first="M Amine" last="Chikh">M Amine Chikh</name><name sortKey="Saidi, Meryem" sort="Saidi, Meryem" uniqKey="Saidi M" first="Meryem" last="Saidi">Meryem Saidi</name></noCountry><country name="Algérie"><noRegion><name sortKey="Settouti, Nesma" sort="Settouti, Nesma" uniqKey="Settouti N" first="Nesma" last="Settouti">Nesma Settouti</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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