Warts and condylomas are benign skin proliferation caused by HPV (human papillomavirus), which can appear anywhere in the body, including the genital regions. One of the treatments used to combat this type of tumor is immunotherapy, a technique that advances the stimulation of the immune system through the use of substances that modify the biological response in human beings. Immunological reactions may be the result of the antigen-antibody interaction or the mechanisms involved in cell-mediated immunity. Health professionals working with these techniques can use specialist systems to assist in the diagnosis of treatment effectiveness in patients. A system based on fuzzy logic was developed with data from medical research. This system can predict the adaptability of a patient to the treatment with 83.33% accuracy. This article proposes the use of a hybrid model of artificial intelligence and fuzzy logic to improve the predictive results of the expert system through the creation of fuzzy rules to construct a more interpretative expert system. Based on the tests performed, we can infer that the proposed model kept the results statistically equal in the prediction of efficiency in the immunotherapeutic treatment, besides making possible the creation of fuzzy rules based on the data of the research on the medication.
Using fuzzy neural networks to the prediction of improvement in expert systems for treatment of immunotherapy
de Campos Souza, P. V.;
2018-01-01
Abstract
Warts and condylomas are benign skin proliferation caused by HPV (human papillomavirus), which can appear anywhere in the body, including the genital regions. One of the treatments used to combat this type of tumor is immunotherapy, a technique that advances the stimulation of the immune system through the use of substances that modify the biological response in human beings. Immunological reactions may be the result of the antigen-antibody interaction or the mechanisms involved in cell-mediated immunity. Health professionals working with these techniques can use specialist systems to assist in the diagnosis of treatment effectiveness in patients. A system based on fuzzy logic was developed with data from medical research. This system can predict the adaptability of a patient to the treatment with 83.33% accuracy. This article proposes the use of a hybrid model of artificial intelligence and fuzzy logic to improve the predictive results of the expert system through the creation of fuzzy rules to construct a more interpretative expert system. Based on the tests performed, we can infer that the proposed model kept the results statistically equal in the prediction of efficiency in the immunotherapeutic treatment, besides making possible the creation of fuzzy rules based on the data of the research on the medication.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
