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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

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Predicting the conversion from clinically isolated syndrome to multiple sclerosis: An explainable machine learning

Saeid Rasouli¹, Mohammad Sedigh Dakkali², Reza Azarbad³

¹School of Medicine, Five Senses Health Research Institute, Hazrat-e Rasool General Hospital, Iran University of Medical Sciences, Tehran, Iran.

Multiple Sclerosis and Related Disorders

|April 20, 2024

Summary

This study developed an explainable machine learning model to predict clinically definite multiple sclerosis (CDMS) conversion from clinically isolated syndrome (CIS). The model accurately identifies high-risk patients, aiding personalized treatment and disability prevention.

Keywords:

Clinically isolated syndrome Explainability Machine learning Model Multiple sclerosis Prediction XGBoost

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Published on: June 26, 2013

Area of Science:

Neurology
Artificial Intelligence
Medical Informatics

Background:

Predicting conversion from clinically isolated syndrome (CIS) to clinically definite multiple sclerosis (CDMS) is crucial for personalized treatment.
Early prediction allows for timely intervention to prevent disability.

Purpose of the Study:

Develop an explainable machine learning (ML) model to predict CIS to CDMS conversion.
Utilize demographic, clinical, and imaging data for prediction.
Enhance transparency in clinical decision-making for multiple sclerosis (MS) patients.

Main Methods:

Employed Extreme Gradient Boosting (XGBoost) on a dataset of 273 Mexican mestizo CIS patients.
Utilized cross-validation for feature selection and a holdout set for testing.
Applied SHapley Additive Explanations (SHAP) for model interpretability.

Main Results:

Identified nine significant predictive variables, including age, symptoms, and imaging/CSF markers.
Achieved cross-validation accuracy of 83.6% and AUC of 91.8%.
Attained test set accuracy of 78.3% and AUC of 85.8%.

Conclusions:

The explainable ML model effectively stratifies risk for CDMS conversion.
Facilitates personalized treatment decisions and disability prevention in MS care.
Provides a numerical risk estimate, improving clinical decision-making transparency.