Abstract #0784

Improving the accuracy of multiple sclerosis classification via robust feature selection based on quantitative tractography

Maria Petracca^1,2, Alberto Azzari³, Antonella Mensi³, Nicole Graziano², Alessandro Daducci³, Manuele Bicego³, Matilde Inglese^2,4,5,6, and Simona Schiavi^3,4

¹Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy, ²Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ³Department of Computer Science, University of Verona, Verona, Italy, ⁴Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy, ⁵Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ⁶IRCCS Ospedale Policlinico San Martino, Genoa, Italy

Synopsis

Improving image-based classification accuracy in multiple sclerosis while characterizing biological relevant features remains an open challenge. To this aim we applied a robust feature selection (FS) procedure based on a leave-one-out cross-validation scheme on structural connectivity features derived from connectomes computed with convex optimization modelling for microstructure informed tractography. We computed classification accuracy for different classifiers (NN, KNN, SVM-LIN, SVM-RBF, RF) before and after the application of the FS procedure. The highest overall accuracy (91%) was obtained using the FS procedure on the whole connectome. The biological meaningfulness of the selected features is supported by their correlations with clinical scores.

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