Poster | 6th Internet World Congress for Biomedical Sciences |
Antonio J. Serrano López(1), Gustavo Camps i Valls(2), EMILIO SORIA OLIVAS(3), NICOLÁS VICTOR JIMÉNEZ TORRÉS(4), José David Martín Guerrero(5), Jose Ramon Sepulveda Sanchis(6)
(1)(6)Dpto. Electrónica. Universidad de Valencia - Burjasot. Spain
(2)Universitat de València - Burjassot, Valencia. Spain
(3)DPTO INGENIERÍA ELECTRONICA. FACULTAD DE FISICAS - BURJASSOT/VALENCIA. Spain
(4)DPTO FARMACIA Y TECNOLOGÍA FARMACEÚTICA. FACULTAD DE FARMACIA - BURJASSOT/VALENCIA. Spain
(5)G.P.D.S. Departament d´Enginyeria Electrònica. Universitat de València - Burjassot. Spain
[Pharmacology] |
[Health Informatics] |
[Oncology] |
The neural network was trained with a subset of 628 samples from the 748 original ones, using several cost functions. Under then we can find quadratic, entropic, error modulus and the one corresponding to Minkowski´s norm. The neural networks were obtained by cross-validation using the rest of the 120 samples.
The performance measure used in this paper is an extension of the concepts of sensibility and specificity broadly used in medicine. The measure of successes has not been used due to the great difference of cases among the three groups.
We define sensibility (Se) and specificity (Sp) as:
The best cost function was the entropic function obtaining the highest values in Se+Sp. (3)
It practically make a correct classification of all the patients of the group PME (patient more harmed by the emesis). Another fact is that the number of connections remainds the smallest one compared to the best nets obtained using the other cost functions.
[Pharmacology] |
[Health Informatics] |
[Oncology] |