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

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.

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

Computational and Mathematical Methods in Medicine ◽

10.1155/2017/8424198 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Juana Canul-Reich ◽

Juan Frausto-Solís ◽

José Hernández-Torruco

Keyword(s):

Predictive Model ◽

Axonal Neuropathy ◽

Classification Performance ◽

Guillain Barre Syndrome ◽

Acute Motor Axonal Neuropathy ◽

Fisher Syndrome ◽

Adequate Treatment ◽

Guillain Barré Syndrome ◽

Subtype Identification ◽

Guillain Barré

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.

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

Computational and Mathematical Methods in Medicine ◽

10.1155/2014/432109 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 8

Author(s):

José Hernández-Torruco ◽

Juana Canul-Reich ◽

Juan Frausto-Solís ◽

Juan José Méndez-Castillo

Keyword(s):

Feature Selection ◽

Clustering Algorithm ◽

Information Gain ◽

Clustering Algorithms ◽

Guillain Barre Syndrome ◽

Computational Techniques ◽

Feature Subset ◽

Guillain Barré Syndrome ◽

Symmetrical Uncertainty ◽

Guillain Barré

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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A Kernel-Based Predictive Model for Guillain-Barré Syndrome

Advances in Artificial Intelligence and Its Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-27101-9_20 ◽

2015 ◽

pp. 270-281

Author(s):

José Hernández-Torruco ◽

Juana Canul-Reich ◽

Juan Frausto-Solis ◽

Juan José Méndez-Castillo

Keyword(s):

Predictive Model ◽

Guillain Barre Syndrome ◽

Guillain Barré Syndrome ◽

Guillain Barré

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

Symmetry ◽

10.3390/sym12030482 ◽

2020 ◽

Vol 12 (3) ◽

pp. 482

Author(s):

Manuel Torres-Vásquez ◽

Oscar Chávez-Bosquez ◽

Betania Hernández-Ocaña ◽

José Hernández-Torruco

Keyword(s):

Predictive Models ◽

Real Data ◽

Machine Learning Algorithms ◽

Guillain Barre Syndrome ◽

Minority Class ◽

Original Dataset ◽

Appropriate Treatment ◽

Binary Approach ◽

Guillain Barré Syndrome ◽

Guillain Barré

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.

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