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

Canul-Reich, Juana; Hernández-Torruco, José; Chávez-Bosquez, Oscar; Hernández-Ocaña, Betania

doi:10.1155/2018/1576927

Cited by 5 publications

(5 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this case, 4 of the 16 features were clinical, and the remaining features came from medical studies. Also, 17 in a predictive model based on the ensemble methods Boosting, Bagging, C5.0, RF, and Random Subspace, reached an accuracy of 0.9366.…”

Section: Introductionmentioning

confidence: 99%

Toward a machine learning model for a primary diagnosis of Guillain-Barré syndrome subtypes

Alarcón-Narváez¹,

Hernández-Torruco²,

Hernández-Ocaña³

et al. 2021

Health Informatics J

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Toward a machine learning model for a primary diagnosis of Guillain-Barré syndrome subtypes

Alarcón-Narváez¹,

Hernández-Torruco²,

Hernández-Ocaña³

et al. 2021

Health Informatics J

Self Cite

View full text Add to dashboard Cite

show abstract

“…Also, to identify GBS subtypes, in the study [17], they developed predictive models using ML and compared their performance with ensemble methods. To classify subtypes, one-versus-all and one-versus-one, three classification experiments are created using real data with relevant features.…”

Section: Review Literaturementioning

confidence: 99%

Diagnosis and treatment of Guillain-Barre using the prolog expert system

Andrade-Arenas,

Molina-Velarde,

Pucuhuayla-Revatta

et al. 2024

IJEECS

View full text Add to dashboard Cite

<span>This research is mostly about Guillain-Barre syndrome (GBS), a complicated neurological condition with many subtypes that make diagnosis and treatment hard, even though medical care is always getting better. The main goal of this study is to build and test an expert system that can correctly diagnose these subtypes, with a focus on early detection and personalized treatments. <br /> The evaluation of the system was carried out using a dataset composed of 20 cases (12 positive and 8 negative). A confusion matrix was used to evaluate key metrics such as precision, sensitivity, and specificity. The findings demonstrate precision and sensitivity of 83% and specificity of 75%. These findings unambiguously demonstrate the efficacy of the system in correctly identifying positive Guillain-Barre cases while substantially reducing false negatives. In conclusion, this expert system offers a potentially useful tool to improve the accuracy of the diagnosis and treatment of Guillain-Barre patients. However, to take advantage of its full potential in clinical practice, it should be used as diagnostic support and not replace the medical staff, and it should be updated periodically to reflect new findings in medicine.</span>

show abstract

“…purpleIn the specialized literature, there are no studies to classify the subtypes of GBS using Machine Learning algorithms. In previous studies, [13,14], predictive models were created to classify the four main GBS subtypes using different classifiers. These models were created using an imbalanced dataset obtained an accuracy of 90%.…”

Section: Imbalanced Data Classificationmentioning

confidence: 99%

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 5 publications

References 28 publications

Toward a machine learning model for a primary diagnosis of Guillain-Barré syndrome subtypes

Toward a machine learning model for a primary diagnosis of Guillain-Barré syndrome subtypes

Diagnosis and treatment of Guillain-Barre using the prolog expert system

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

Contact Info

Product

Resources

About