286 Int. J. Medical Engineering and Informatics, Vol. 11, No. 3, 2019 Copyright © 2019 Inderscience Enterprises Ltd. Product unit neural network trained by an evolutionary algorithm for diabetes disease diagnosis Radhwane Benali*, Nabil Dib and Fethi Bereksi Reguig Biomedical Engineering Laboratory, Faculty of Technology, Abou Bekr Belkaid University, Tlemcen, Algeria Email: r_benali@mail.univ-tlemcen.dz Email: nb_dib@mail.univ-tlemven.dz Email: bereksif@yahoo.fr *Corresponding author Abstract: Diabetes disease occurs when the level of glucose in the blood becomes higher than normal because the body is unable to produce the insulin which is needed to regulate glucose. In this study, a new classification method for the diagnosis of diabetes disease was developed. This method is based on a special class of neural network known as product-unit neural networks (PUNN) which was trained by an evolutionary algorithm (EA). We have used EA in order to determine the basic topology of the structure of the PUNN, and to estimate its coefficients weights. The performances of the proposed classifier were evaluated through the sensitivity, the specificity and the classification accuracy using both conventional and 10-fold cross-validation method using the Pima Indian diabetes (PID) dataset. Obtained results reveal that the proposed approach outperforms several famous and recent methods existing in the literature for diabetes disease diagnosis. Keywords: product unit neural network; PUNN; evolutionary algorithms; EA; diabetes disease diagnosis; Pima Indian diabetes; PID; medical informatics. Reference to this paper should be made as follows: Benali, R., Dib, N. and Bereksi Reguig, F. (2019) ‘Product unit neural network trained by an evolutionary algorithm for diabetes disease diagnosis’, Int. J. Medical Engineering and Informatics, Vol. 11, No. 3, pp.286–298. Biographical notes: Radhwane Benali received his Engineering, MSc and PhD in Biomedical Electronics from the University of Abou Bekr Belkaid of Tlemcen, Algeria in 2005, 2008 and 2013 respectively. Currently, he is a member of Biomedical Engineering Laboratory (Laboratoire GBM). His area of research interests includes biomedical signal processing, machine learning and medical data analysis. Nabil Dib received his Engineering, MSc and PhD in Biomedical Electronics from the University of Abou Bekr Belkaid of Tlemcen, Algeria in 2005, 2009 and 2014 respectively. Currently, he is a member of Biomedical Engineering Laboratory (Laboratoire GBM). His area of research interests includes biomedical signal processing and medical data analysis.