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A Predictive Model for Guillain-Barré Syndrome Based on Single Learning Algorithms
Juana Canul-Reich1, Juan Frausto-Solís2, José Hernández-Torruco1
1División Académica de Informática y Sistemas, Universidad Juárez Autónoma de Tabasco, Km. 1 Carretera Cunduacán, Jalpa de Méndez, Col. Esmeralda, CP 86690, Cunduacán, TAB, Mexico.
Computational and Mathematical Methods in Medicine
|May 11, 2017
Summary
This study developed predictive models for Guillain-Barré Syndrome (GBS) subtypes, achieving high accuracy. The research identified optimal classifiers for accurate GBS diagnosis and treatment.
Area of Science:
- Neurology
- Machine Learning
- Medical Diagnostics
Background:
- Guillain-Barré Syndrome (GBS) is a severe autoimmune neurological disorder with four main subtypes.
- Accurate GBS subtype identification is crucial for timely and effective patient treatment.
Purpose of the Study:
- To develop and evaluate predictive models for classifying Guillain-Barré Syndrome subtypes.
- To identify the most effective single classifiers for GBS subtype prediction.
Main Methods:
- Experimentation with 15 single classifiers using a dataset of 16 relevant features.
- Classification performed in two scenarios: four-subtype classification and One Versus All (OvA).
- Performance evaluated using 10-fold cross-validation and statistical analysis.
Main Results:
- Over half of the classifiers achieved an average accuracy exceeding 0.90 in the four-subtype classification.
- In OvA classification, subtypes with larger datasets yielded superior classification outcomes.
- Statistical tests identified top-performing classifiers for each classification scenario.
Conclusions:
- This study presents a comprehensive approach to building predictive models for GBS subtypes.
- The findings offer valuable insights into selecting the best classifiers for GBS diagnosis.
