TY - JOUR
T1 - An associative memory approach to medical decision support systems
AU - Aldape-Pérez, Mario
AU - Yáñez-Márquez, Cornelio
AU - Camacho-Nieto, Oscar
AU - J.Argüelles-Cruz, Amadeo
N1 - Funding Information:
The authors of the present paper would like to thank the following institutions for their economical support to develop this work: Science and Technology National Council of Mexico (CONACyT Grant No. 174952), SNI, National Polytechnic Institute of Mexico (COFAA, SIP, ESCOM, and CIC) and ICyTDF (Grant No. PIUTE10-77 and PICSO10-85).
Funding Information:
This work was supported by the Science and Technology National Council of Mexico under Grant No. 174952, by the National Polytechnic Institute of Mexico (Project No. SIP-IPN 20101709) and by the ICyTDF (Grant No. PIUTE10-77 and PICSO10-85)
PY - 2012/6
Y1 - 2012/6
N2 - Classification is one of the key issues in medical diagnosis. In this paper, a novel approach to perform pattern classification tasks is presented. This model is called Associative Memory based Classifier (AMBC). Throughout the experimental phase, the proposed algorithm is applied to help diagnose diseases; particularly, it is applied in the diagnosis of seven different problems in the medical field. The performance of the proposed model is validated by comparing classification accuracy of AMBC against the performance achieved by other twenty well known algorithms. Experimental results have shown that AMBC achieved the best performance in three of the seven pattern classification problems in the medical field. Similarly, it should be noted that our proposal achieved the best classification accuracy averaged over all datasets.
AB - Classification is one of the key issues in medical diagnosis. In this paper, a novel approach to perform pattern classification tasks is presented. This model is called Associative Memory based Classifier (AMBC). Throughout the experimental phase, the proposed algorithm is applied to help diagnose diseases; particularly, it is applied in the diagnosis of seven different problems in the medical field. The performance of the proposed model is validated by comparing classification accuracy of AMBC against the performance achieved by other twenty well known algorithms. Experimental results have shown that AMBC achieved the best performance in three of the seven pattern classification problems in the medical field. Similarly, it should be noted that our proposal achieved the best classification accuracy averaged over all datasets.
KW - Associative memories
KW - Decision support systems
KW - Pattern classification
KW - Supervised Machine Learning algorithms
UR - http://www.scopus.com/inward/record.url?scp=84860246796&partnerID=8YFLogxK
U2 - 10.1016/j.cmpb.2011.05.002
DO - 10.1016/j.cmpb.2011.05.002
M3 - Artículo
C2 - 21703713
SN - 0169-2607
VL - 106
SP - 287
EP - 307
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
IS - 3
ER -