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Decoding Wilson disease: a machine learning approach to predict neurological symptoms.

Yulong Yang¹, Gang-Ao Wang², Shuzhen Fang¹

¹Department of Neurology, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, Anhui, China.

Frontiers in Neurology

|July 5, 2024

Summary

This study developed a machine learning model to predict neurological symptoms in Wilson disease (WD) patients. Key predictors include brainstem damage, creatinine, age, indirect bilirubin, and ceruloplasmin, aiding clinical decisions.

Keywords:

Wilson disease eXtreme Gradient Boosting (XGB)machine learning neurological symptom prediction model

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Area of Science:

Medical research
Computational biology
Genetics

Background:

Wilson disease (WD) is a rare genetic disorder affecting copper metabolism.
Neurological manifestations are a common and significant clinical feature of WD.
Accurate prediction of neurological symptoms is crucial for timely intervention.

Purpose of the Study:

To develop and validate a machine learning model for predicting neurological symptoms in Wilson disease patients.
To identify key clinical indicators associated with the occurrence of neurological symptoms in WD.
To leverage multidimensional clinical data for improved diagnostic and prognostic capabilities.

Main Methods:

Utilized machine learning, specifically eXtreme Gradient Boosting (XGB), to build a predictive model.
Collected comprehensive clinical data including general information, imaging, laboratory tests, and clinical scales from 185 WD patients.
Employed the SHAP method for feature importance analysis to understand predictor contributions.

Main Results:

The XGB model demonstrated high predictive performance (MCC: 0.556, ACC: 0.929, AUROC: 0.835, AUPRC: 0.975).
Identified brainstem damage, blood creatinine (Cr), age, indirect bilirubin (IBIL), and ceruloplasmin (CP) as the top five predictors.
Established correlations: brainstem damage, higher Cr, age, and IBIL increase likelihood of neurological symptoms; lower CP decreases it.

Conclusions:

The developed machine learning model accurately predicts neurological symptoms in Wilson disease.
Brainstem damage, Cr, age, IBIL, and CP are critical indicators for predicting neurological involvement in WD.
This model offers valuable support for clinical decision-making in managing Wilson disease.