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Abstract #0604

Disability Prediction in Multiple Sclerosis using Ensemble of Machine Learning Models and DTI Brain Connectivity

Berardino Barile1, Aldo Marzullo2, Claudio Stamile3, Françoise Durand-Dubief4, and Dominique Sappey-Marinier1,5
1CREATIS (UMR 5220 CNRS & U1206 INSERM), Université Claude Bernard Lyon 1, Villeurbanne, France, 2Department of Mathematics and Computer Science, University of Calabria, Rende, Italy, 3R&D Department, CGnal, Milan, Italy, 4Hôpital Neurologique, Hospices Civils de Lyon, Bron, France, 5MRI, CERMEP - Imagerie du Vivant, Bron, France

The Expanded Disability Status Scale (EDSS) monitors physical impairment in Multiple Sclerosis (MS). A Staking Ensemble model composed of 4 ML "boosting" models was used to predict EDSS using both white matter (WM) fiber-bundles and structural connectome data. This model provided excellent prediction results with an RMSE of 0.92 to 1.08. A counterfactual model was added to highlight the most important WM links and fiber-bundles in the prediction process. The accordance of the findings obtained with both data types confirmed the clinical interest of such methods for disability prediction using DTI data.

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