Machine learning classificatory as a tool in the diagnosis of amyotrophic lateral sclerosis using diffusion tensor imaging parameters collected with 1.5T MRI scanner: A case study
Full Text (PDF)
Milosz Jamrozy 1 * , Edyta Maj 1 , Maksymilian Bielecki 2 , Marta Bartoszek 3 , Marek Golebiowski 1 , Magdalena Kuzma-Kozakiewicz 4
More Detail
1 Department of Clinical Radiology, Medical University of Warsaw, Warsaw, POLAND2 Department of Psychology, SWPS University of Social Sciences and Humanities, Warsaw, POLAND3 Department of Pediatric Radiology, University Clinical Center of the Medical University of Warsaw, Warsaw, POLAND4 Department of Neurology, Medical University of Warsaw, Warsaw, POLAND* Corresponding Author

Abstract

The relevance of the study lies in the need to improve the diagnosis of amyotrophic lateral sclerosis (ALS) by utilizing diffusion tensor imaging (DTI) obtained from conventional 1.5 Tesla MRI scanners. The study aimed to investigate the potential of using different machine learning (ML) classifiers to distinguish between individuals with ALS. In this study, five ML classifiers (“support vector machine (SVM)”, “k-nearest neighbors (K-NN)”, naïve Bayesian classifier, “decision tree”, and “decision forest”) were used, based on two DTI parameters: fractional anisotropy and apparent diffusion coefficient, obtained from two manually selected ROIs at the level of the brain pyramids in 47 ALS patients and 55 healthy subjects. The quality of each classifier was evaluated using the confusion matrix and ROC curves. The highest accuracy in differentiating ALS patients from healthy individuals based on DTI data was demonstrated by the radial kernel support vector method (77% accuracy [p=0.01]), while K-NN and “decision tree” classifiers had slightly lower performance, and “decision forest” classifier was overtrained to the training set (AUC=1). The authors have shown a sufficiently accuracy of ML classifier “SVM” in detecting radiological characteristics of ALS in pyramidal tracts.

Keywords

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Original Article

ELECTRON J GEN MED, Volume 20, Issue 6, December 2023, Article No: em535

https://doi.org/10.29333/ejgm/13536

Publication date: 01 Nov 2023

Online publication date: 06 Aug 2023

Article Views: 1302

Article Downloads: 973

Open Access References How to cite this article