Guillain-Barré Syndrome (GBS) is a neurological disorder affecting people of any age and sex, mainly damaging the peripheral nervous system. GBS is divided into several subtypes, in which only four are the most common, demanding different treatments. Identifying the subtype is an expensive and time-consuming task. Early GBS detection is crucial to save the patient’s life and not aggravate the disease. This work aims to provide a primary screening tool for GBS subtypes fast and efficiently without complementary invasive methods, based only on clinical variables prospected in consultation, taken from clinical history, and based on risk factors. We conducted experiments with four classifiers with different approaches, five different filters for feature selection, six wrappers, and One versus All (OvA) classification. For the experiments, we used a data set that includes 129 records of Mexican patients and 26 clinical representative variables. Random Forest filter obtained the best results in each classifier for the diagnosis of the four subtypes, in the same way, this filter with the SVM classifier achieved the best result (0.6840). OvA with SVM classifier reached a balanced accuracy of 0.8884 for the Miller-Fisher (MF) subtype.

This document describes the analysis carried out in terms of execution time, processing and use of random access memory, to characterize the efficiency of two matrix numerical methods, which are Gauss-Jacques with implicit Euclidean modularization and Gauss-Jordan with explicit modularization. Both methods compute the modular inverse of any given matrix in Zn. The initial matrix is known as the Key in the context of symmetric cryptography. The tests carried out considered multiple matrix-size in order to allow us identify the behavior of each method, and the resources that each one uses in terms of processing and memory to determine which is the most efficient method in the computational context.

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