Machine learning diagnostic model for amyotrophic lateral sclerosis analysis using MRI-derived features.
Authors
Affiliations (5)
Affiliations (5)
- Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Valencia, Spain.
- Medical Imaging Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain. [email protected].
- Biomedical Imaging Research Group, La Fe Health Research Institute, Valencia, Spain.
- ALS Unit, Department of Neurology, Hospital Universitari i Politècnic La Fe, Valencia, Spain.
- Medical Imaging Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain.
Abstract
Amyotrophic Lateral Sclerosis is a devastating motor neuron disease characterized by its diagnostic difficulty. Currently, no reliable biomarkers exist in the diagnosis process. In this scenario, our purpose is the application of machine learning algorithms to imaging MRI-derived variables for the development of diagnostic models that facilitate and shorten the process. A dataset of 211 patients (114 ALS, 45 mimic, 22 genetic carriers and 30 control) with MRI-derived features of volumetry, cortical thickness and local iron (via T2* mapping, and visual assessment of susceptibility imaging). A binary classification task approach has been taken to classify patients with and without ALS. A sequential modeling methodology, understood from an iterative improvement perspective, has been followed, analyzing each group's performance separately to adequately improve modelling. Feature filtering techniques, dimensionality reduction techniques (PCA, kernel PCA), oversampling techniques (SMOTE, ADASYN) and classification techniques (logistic regression, LASSO, Ridge, ElasticNet, Support Vector Classifier, K-neighbors, random forest) were included. Three subsets of available data have been used for each proposed architecture: a subset containing automatic retrieval MRI-derived data, a subset containing the variables from the visual analysis of the susceptibility imaging and a subset containing all features. The best results have been attained with all the available data through a voting classifier composed of five different classifiers: accuracy = 0.896, AUC = 0.929, sensitivity = 0.886, specificity = 0.929. These results confirm the potential of ML techniques applied to imaging variables of volumetry, cortical thickness, and local iron for the development of diagnostic model as a clinical tool for decision-making support.