### Abstract

Lingua originale | English |
---|---|

pagine (da-a) | 100-106 |

Numero di pagine | 7 |

Rivista | Internal and Emergency Medicine |

Volume | 2007 |

Stato di pubblicazione | Published - 2007 |

### All Science Journal Classification (ASJC) codes

- Internal Medicine
- Emergency Medicine

### Cita questo

*Internal and Emergency Medicine*,

*2007*, 100-106.

**Probabilistic and Fuzzy logic in clinical diagnosis.** / Licata, Gaetano; Licata, Gaetano.

Risultato della ricerca: Article

*Internal and Emergency Medicine*, vol. 2007, pagg. 100-106.

}

TY - JOUR

T1 - Probabilistic and Fuzzy logic in clinical diagnosis

AU - Licata, Gaetano

AU - Licata, Gaetano

PY - 2007

Y1 - 2007

N2 - In this study I have compared classic and fuzzy logic and their usefulness in clinical diagnosis. The theory of probability is often considered a device to protect the classical two-valued logic from the evidence of its inadequacy to understand and show the complexity of world [1]. This can be true, but it is not possible to discard the theory of probability. I will argue that the problems and the application fields of the theory of probability are very different from those of fuzzy logic. After the introduction on the theoretical bases of fuzzy approach to logic, I have reported some diagnostic argumentations employing fuzzy logic. The state of normality and the state of disease often fight their battle on scalar quantities of biological values and it is not hard to establish a correspondence between the biological values and the percent values of fuzzy logic. Accordingly, I have suggested some applications of fuzzy logic in clinical diagnosis and in particular I have utilised a fuzzy curve to recognise subjects with diabetes mellitus, renal failure and liver disease. The comparison between classic and fuzzy logic findings seems to indicate that fuzzy logic is more adequate to study the development of biological events. In fact, fuzzy logic is useful when we have a lot of pieces of information and when we dispose to scalar quantities. In conclusion, increasingly the development of technology offers new instruments to measure pathological parameters through scalar quantities, thus it is reasonable to think that in the future fuzzy logic will be employed more in clinical diagnosis.

AB - In this study I have compared classic and fuzzy logic and their usefulness in clinical diagnosis. The theory of probability is often considered a device to protect the classical two-valued logic from the evidence of its inadequacy to understand and show the complexity of world [1]. This can be true, but it is not possible to discard the theory of probability. I will argue that the problems and the application fields of the theory of probability are very different from those of fuzzy logic. After the introduction on the theoretical bases of fuzzy approach to logic, I have reported some diagnostic argumentations employing fuzzy logic. The state of normality and the state of disease often fight their battle on scalar quantities of biological values and it is not hard to establish a correspondence between the biological values and the percent values of fuzzy logic. Accordingly, I have suggested some applications of fuzzy logic in clinical diagnosis and in particular I have utilised a fuzzy curve to recognise subjects with diabetes mellitus, renal failure and liver disease. The comparison between classic and fuzzy logic findings seems to indicate that fuzzy logic is more adequate to study the development of biological events. In fact, fuzzy logic is useful when we have a lot of pieces of information and when we dispose to scalar quantities. In conclusion, increasingly the development of technology offers new instruments to measure pathological parameters through scalar quantities, thus it is reasonable to think that in the future fuzzy logic will be employed more in clinical diagnosis.

KW - Probabilistic logic, Fuzzy logic, truth, diagnosis

UR - http://hdl.handle.net/10447/49643

M3 - Article

VL - 2007

SP - 100

EP - 106

JO - Internal and Emergency Medicine

JF - Internal and Emergency Medicine

SN - 1828-0447

ER -