16 research outputs found

    A Predictive Model for Guillain-Barré Syndrome Based on Single Learning Algorithms

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    Background. Guillain-Barré Syndrome (GBS) is a potentially fatal autoimmune neurological disorder. The severity varies among the four main subtypes, named as Acute Inflammatory Demyelinating Polyneuropathy (AIDP), Acute Motor Axonal Neuropathy (AMAN), Acute Motor Sensory Axonal Neuropathy (AMSAN), and Miller-Fisher Syndrome (MF). A proper subtype identification may help to promptly carry out adequate treatment in patients. Method. We perform experiments with 15 single classifiers in two scenarios: four subtypes’ classification and One versus All (OvA) classification. We used a dataset with the 16 relevant features identified in a previous phase. Performance evaluation is made by 10-fold cross validation (10-FCV). Typical classification performance measures are used. A statistical test is conducted in order to identify the top five classifiers for each case. Results. In four GBS subtypes’ classification, half of the classifiers investigated in this study obtained an average accuracy above 0.90. In OvA classification, the two subtypes with the largest number of instances resulted in the best classification results. Conclusions. This study represents a comprehensive effort on creating a predictive model for Guillain-Barré Syndrome subtypes. Also, the analysis performed in this work provides insight about the best single classifiers for each classification case

    Impacto de los algoritmos de sobremuestreo en la clasificación de subtipos principales del síndrome de Guillain-Barré

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    El síndrome de Guillain-Barré es un trastorno neurológico donde el sistema inmune del cuerpo ataca al sistema nervioso periférico. Esta enfermedad es de rápida evolución y es la causa más frecuente de parálisis del cuerpo. Existen cuatro variantes de SGB: polineuropatía desmielinizante inflamatoria aguda, neuropatía axonal motora aguda, neuropatía axonal sensorial aguda y síndrome de Miller-Fisher. Identificar el subtipo de SGB que el paciente contrajo es determinante debido a que el tratamiento es diferente para cada subtipo. El objetivo de este estudio fue determinar cuál algoritmo de sobremuestreo mejora el rendimiento de los clasificadores. Además, determinar si balancear los datos mejoran el rendimiento de los modelos predictivos. Aplicamos tres métodos de sobremuestro (ROS, SMOTE y ADASYN) a la clase minoritaria, utilizamos tres clasificadores (C4.5, SVM y JRip). El rendimiento de los modelos se obtuvo mediante la curva ROC. Los resultados muestran que balancear el dataset mejora el rendimiento de los modelos predictivos. El algoritmo SMOTE fue el mejor método de balanceo en combinación con el clasificador JRip para OVO y el clasificador C4.5 para OVA.//Guillain-Barré Syndrome (GBS) is a neurological disorder where the body’s immune system attacks the peripheral nervous system. This disease evolves rapidly and is the most frequent cause of paralysis of the body. There are four variants of GBS: Acute Inflammatory Demyelinating Polyneuropathy, Acute Motor Axonal Neuropathy, Acute Sensory Axial Neuropathy, and Miller-Fisher Syndrome. Identifying the GBS subtype that the patient has is decisive because the treatment is different for each subtype. The objective of this study was to determine which oversampling algorithm improves classifier performance. In addition, to determine whether balancing the data improves the performance of the predictive models. Three oversampling methods (ROS, SMOTE, and ADASYN) were applied to the minority class. Three classifiers (C4.5, SVM and JRip) were used. The performance of the models was obtained using the ROC curve. Results show that balancing the dataset improves the performance of the predictive models. The SMOTE Algorithm was the best balancing method, in combination with the classifier JRip for OVO and the classifier C4.5 for OVA

    Classification of Cyber-Aggression Cases Applying Machine Learning

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    The adoption of electronic social networks as an essential way of communication has become one of the most dangerous methods to hurt people’s feelings. The Internet and the proliferation of this kind of virtual community have caused severe negative consequences to the welfare of society, creating a social problem identified as cyber-aggression, or in some cases called cyber-bullying. This paper presents research to classify situations of cyber-aggression on social networks, specifically for Spanish-language users of Mexico. We applied Random Forest, Variable Importance Measures (VIMs), and OneR to support the classification of offensive comments in three particular cases of cyber-aggression: racism, violence based on sexual orientation, and violence against women. Experimental results with OneR improve the comment classification process of the three cyber-aggression cases, with more than 90% accuracy. The accurate classification of cyber-aggression comments can help to take measures to diminish this phenomenon

    Impacto de los algoritmos de sobremuestreo en la clasificación de subtipos principales del síndrome de guillain-barré

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    Guillain-Barré Syndrome (GBS) is a neurological disorder where the body’s immune system attacks the peripheral nervous system. This disease evolves rapidly and is the most frequent cause of paralysis of the body. There are four variants of GBS: Acute Inflammatory Demyelinating Polyneuropathy, Acute Motor Axonal Neuropathy, Acute Sensory Axial Neuropathy, and Miller-Fisher Syndrome. Identifying the GBS subtype that the patient has is decisive because the treatment is different for each subtype. The objective of this study was to determine which oversampling algorithm improves classifier performance. In addition, to determine whether balancing the data improves the performance of the predictive models. Three oversampling methods (ROS, SMOTE, and ADASYN) were applied to the minority class. Three classifiers (C4.5, SVM and JRip) were used. The performance of the models was obtained using the ROC curve. Results show that balancing the dataset improves the performance of the predictive models. The SMOTE Algorithm was the best balancing method, in combination with the classifier JRip for OVO and the classifier C4.5 for OVA.El Síndrome de Guillain-Barré es un trastorno neu-rológico donde el sistema inmune del cuerpo ataca al sistema nervioso periférico. Esta enfermedad es de rápida evolución y es la causa más frecuente de parálisis del cuerpo. Existen cuatro variantes de SGB: Polineuropatía Desmielinizante Inflamatoria Aguda, Neuropatía Axonal Motora Aguda, Neuropatía Axonal Sensorial Aguda y Síndrome de Miller-Fisher. Identificar el subtipo de SGB que el paciente contrajo es determinante debido a que el tratamiento es diferente para cada subtipo. El objetivo de este estudio fue determinar cuál algoritmo de sobremuestreo mejora el rendimiento de los clasificadores. Además, determinar si balancear los datos mejoran el rendimiento de los modelos predictivos. Aplicamos tres métodos de sobremuestro (ROS, SMOTE y ADASYN) a la clase minoritaria, utilizamos tres clasificadores (C4.5,SVM y JRip). El rendimiento de los modelos se obtuvo mediante la curva ROC. Los resultados muestran que balancear el dataset mejora el rendimiento de los modelos predictivos. El algoritmo SMOTE fue el mejor método de balanceo en combinación con el clasificador JRip para OVO y el clasificador C4.5para OVA

    Classification of Guillain–Barré Syndrome Subtypes Using Sampling Techniques with Binary Approach

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    Guillain–Barré Syndrome (GBS) is an unusual disorder where the body’s immune system affects the peripheral nervous system. GBS has four main subtypes, whose treatments vary among them. Severe cases of GBS can be fatal. This work aimed to investigate whether balancing an original GBS dataset improves the predictive models created in a previous study. purpleBalancing a dataset is to pursue symmetry in the number of instances of each of the classes.The dataset includes 129 records of Mexican patients diagnosed with some subtype of GBS. We created 10 binary datasets from the original dataset. Then, we balanced these datasets using four different methods to undersample the majority class and one method to oversample the minority class. Finally, we used three classifiers with different approaches to creating predictive models. The results show that balancing the original dataset improves the previous predictive models. The goal of the predictive models is to identify the GBS subtypes applying Machine Learning algorithms. It is expected that specialists may use the model to have a complementary diagnostic using a reduced set of relevant features. Early identification of the subtype will allow starting with the appropriate treatment for patient recovery. This is a contribution to exploring the performance of balancing techniques with real data

    A Predictive Model for Guillain–Barré Syndrome Based on Ensemble Methods

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    Nowadays, Machine Learning methods have proven to be highly effective on the identification of various types of diseases, in the form of predictive models. Guillain–Barré syndrome (GBS) is a potentially fatal autoimmune neurological disorder that has barely been studied with computational techniques and few predictive models have been proposed. In a previous study, single classifiers were successfully used to build a predictive model. We believe that a predictive model is imperative to carry out adequate treatment in patients promptly. We designed three classification experiments: (1) using all four GBS subtypes, (2) One versus All (OVA), and (3) One versus One (OVO). These experiments use a real-world dataset with 129 instances and 16 relevant features. Besides, we compare five state-of-the-art ensemble methods against 15 single classifiers with 30 independent runs. Standard performance measures were used to obtain the best classifier in each experiment. Derived from the experiments, we conclude that Random Forest showed the best results in four GBS subtypes classification, no ensemble method stood out over the rest in OVA classification, and single classifiers outperformed ensemble methods in most cases in OVO classification. This study presents a novel predictive model for classification of four subtypes of Guillain–Barré syndrome. Our model identifies the best method for each classification case. We expect that our model could assist specialized physicians as a support tool and also could serve as a basis to improved models in the future

    JMetaBFOP: A tool for solving global optimization problems

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    The Two-Swim Modified Bacterial Foraging Optimization Algorithm (TS-MBFOA) is a bio-inspired algorithm that emulates the foraging behavior of E. Coli bacteria to solve optimization problems. JMetaBFOP (Bacterial foraging-based METAheuristics For Optimization Problems) is a framework implementing the TS-MBFOA processes as a library to solve optimization problems with preloaded constraints or defined by the end user. This paper presents the framework’s design using the Unified Modeling Language (UML), the implementation of a user interface (UI) in the Java platform, and the use of a mathematical expression evaluator called mXparser. JMetaBFOP allows faster calibration of TS-MBFOA parameters with the help of the UI; it eases the experimental design setup, visualization, and evaluation of feasible and optimal results for different optimization problems with constraints, such as benchmarks and particular problems. The framework was tested in 24 test problems with results: competitive in 14 problems, feasible in 7 ones, and no feasible solutions in 3 highly constrained problems. JMetaBFOP is an open-source project available on the GitHub platform

    Supervised Deep Learning Techniques for Image Description: A Systematic Review

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    Automatic image description, also known as image captioning, aims to describe the elements included in an image and their relationships. This task involves two research fields: computer vision and natural language processing; thus, it has received much attention in computer science. In this review paper, we follow the Kitchenham review methodology to present the most relevant approaches to image description methodologies based on deep learning. We focused on works using convolutional neural networks (CNN) to extract the characteristics of images and recurrent neural networks (RNN) for automatic sentence generation. As a result, 53 research articles using the encoder-decoder approach were selected, focusing only on supervised learning. The main contributions of this systematic review are: (i) to describe the most relevant image description papers implementing an encoder-decoder approach from 2014 to 2022 and (ii) to determine the main architectures, datasets, and metrics that have been applied to image description

    Feature Selection for Better Identification of Subtypes of Guillain-Barré Syndrome

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    Guillain-Barré syndrome (GBS) is a neurological disorder which has not been explored using clustering algorithms. Clustering algorithms perform more efficiently when they work only with relevant features. In this work, we applied correlation-based feature selection (CFS), chi-squared, information gain, symmetrical uncertainty, and consistency filter methods to select the most relevant features from a 156-feature real dataset. This dataset contains clinical, serological, and nerve conduction tests data obtained from GBS patients. The most relevant feature subsets, determined with each filter method, were used to identify four subtypes of GBS present in the dataset. We used partitions around medoids (PAM) clustering algorithm to form four clusters, corresponding to the GBS subtypes. We applied the purity of each cluster as evaluation measure. After experimentation, symmetrical uncertainty and information gain determined a feature subset of seven variables. These variables conformed as a dataset were used as input to PAM and reached a purity of 0.7984. This result leads to a first characterization of this syndrome using computational techniques
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