The glycemic index (GI) is a qualitative indicator of the glycemic response of a carbohydrate food. Its variability is due to the composition of the food, which in turn is related to the technology applied to it. This study describes a data processing analysis method that allows the GI of food to be accurately predicted using a model. Data from the food composition table, combined with information from the table of GI values of foods, are processed using an artificial neural network (ANN) to produce a predicted value for the GI of the food. For the samples studied (n = 30), consisting of a variety of traditional dishes (base component ± accompanying sauce), r2 = 0.968, and the learning root mean square error analysis (learning RMSE) tends towards 0. The 7-9-1 neural structure (7 neurons in the input layer, 9 neurons in the hidden layer, and 1 neuron in the output layer) is the most appropriate neural model. During the test phase, it showed the highest R2, indicating a good predictive ability for the ANN method. These results suggest that the selected ANN has a good capacity to respond satisfactorily to an input that is not part of the data from the learning phase. This method, which is fast and inexpensive, compared to in vivo tests, is a valuable tool for predicting the GI of Ivorian traditional foods more effectively.
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