Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification
The use of artificial intelligence techniques such as fuzzy logic, neural networks and evolutionary computation is currently very important in medicine to be able to provide an effective and timely diagnosis. The use of fuzzy logic allows to design fuzzy classifiers, which have fuzzy rules and membe...
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MDPI AG
2019-01-01
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| Online Access: | http://www.mdpi.com/2075-1680/8/1/8 |
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| author | Juan Carlos Guzmán Ivette Miramontes Patricia Melin German Prado-Arechiga |
| author_facet | Juan Carlos Guzmán Ivette Miramontes Patricia Melin German Prado-Arechiga |
| author_sort | Juan Carlos Guzmán |
| collection | DOAJ |
| description | The use of artificial intelligence techniques such as fuzzy logic, neural networks and evolutionary computation is currently very important in medicine to be able to provide an effective and timely diagnosis. The use of fuzzy logic allows to design fuzzy classifiers, which have fuzzy rules and membership functions, which are designed based on the experience of an expert. In this particular case a fuzzy classifier of Mamdani type was built, with 21 rules, with two inputs and one output and the objective of this classifier is to perform blood pressure level classification based on knowledge of an expert which is represented in the fuzzy rules. Subsequently different architectures were made in type-1 and type-2 fuzzy systems for classification, where the parameters of the membership functions used in the design of each architecture were adjusted, which can be triangular, trapezoidal and Gaussian, as well as how the fuzzy rules are optimized based on the ranges established by an expert. The main contribution of this work is the design of the optimized interval type-2 fuzzy system with triangular membership functions. The final type-2 system has a better classification rate of 99.408% than the type-1 classifier developed previously in “Design of an optimized fuzzy classifier for the diagnosis of blood pressure with a new computational method for expert rule optimization” with 98%. In addition, we also obtained a better classification rate than the other architectures proposed in this work. |
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| format | Article |
| id | doaj.art-89524db476b34ae5ad85726c4b95e5fb |
| institution | Directory Open Access Journal |
| issn | 2075-1680 |
| language | English |
| last_indexed | 2024-12-21T16:16:51Z |
| publishDate | 2019-01-01 |
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| spelling | doaj.art-89524db476b34ae5ad85726c4b95e5fb2022-12-21T18:57:40ZengMDPI AGAxioms2075-16802019-01-0181810.3390/axioms8010008axioms8010008Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level ClassificationJuan Carlos Guzmán0Ivette Miramontes1Patricia Melin2German Prado-Arechiga3Tijuana Institute of Technology, Calzada Tecnologico s/n, Fracc. Tomas Aquino, Baja California, Tijuana 22379, MexicoTijuana Institute of Technology, Calzada Tecnologico s/n, Fracc. Tomas Aquino, Baja California, Tijuana 22379, MexicoTijuana Institute of Technology, Calzada Tecnologico s/n, Fracc. Tomas Aquino, Baja California, Tijuana 22379, MexicoCardiodiagnostico Excel Medial Center, Tijuana 22010, MexicoThe use of artificial intelligence techniques such as fuzzy logic, neural networks and evolutionary computation is currently very important in medicine to be able to provide an effective and timely diagnosis. The use of fuzzy logic allows to design fuzzy classifiers, which have fuzzy rules and membership functions, which are designed based on the experience of an expert. In this particular case a fuzzy classifier of Mamdani type was built, with 21 rules, with two inputs and one output and the objective of this classifier is to perform blood pressure level classification based on knowledge of an expert which is represented in the fuzzy rules. Subsequently different architectures were made in type-1 and type-2 fuzzy systems for classification, where the parameters of the membership functions used in the design of each architecture were adjusted, which can be triangular, trapezoidal and Gaussian, as well as how the fuzzy rules are optimized based on the ranges established by an expert. The main contribution of this work is the design of the optimized interval type-2 fuzzy system with triangular membership functions. The final type-2 system has a better classification rate of 99.408% than the type-1 classifier developed previously in “Design of an optimized fuzzy classifier for the diagnosis of blood pressure with a new computational method for expert rule optimization” with 98%. In addition, we also obtained a better classification rate than the other architectures proposed in this work.http://www.mdpi.com/2075-1680/8/1/8type-2 fuzzy logicneural networksgenetic algorithmsfuzzy logicblood pressure |
| spellingShingle | Juan Carlos Guzmán Ivette Miramontes Patricia Melin German Prado-Arechiga Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification Axioms type-2 fuzzy logic neural networks genetic algorithms fuzzy logic blood pressure |
| title | Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification |
| title_full | Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification |
| title_fullStr | Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification |
| title_full_unstemmed | Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification |
| title_short | Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification |
| title_sort | optimal genetic design of type 1 and interval type 2 fuzzy systems for blood pressure level classification |
| topic | type-2 fuzzy logic neural networks genetic algorithms fuzzy logic blood pressure |
| url | http://www.mdpi.com/2075-1680/8/1/8 |
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