Building a neuro-fuzzy model for predicting the bacteria bound and internalized by the white blood cells

In this paper, a model to reflect the immunological properties of white blood cells is created. White blood cells kill infectious bacteria. Two kinds of white blood cells - CHO-IIA and THP1 are studied. Bacteria of sizes 5mum and 0.05mum, and an amount of catalytic agent MbetaCD is added to the white blood cell. Experiments are conducted first by keeping the bacteria size fixed and varying the amount of MbetaCD to observe their effects on bacteria bound to a cell, and then bacteria internalized by a cell. Later the same experiment is repeated with a different bacteria size. The data collected is used to create a model with inbuilt rules, based on fuzzy logic. The model is trained with back propagation using neuro-fuzzy techniques, in order to improve the model. The results obtained by the model are compared with the actual observed data to compare the efficiency of the model. With training, the Root Mean Squared Error reduced from 0.4099 to 0.3705. Complete software with a Graphical User Interface is built to easily display the output of the model, from user-given input.