Abstract
Multiple Sclerosis (MS) is characterized by complex and heterogeneous nature and as a result, there’s currently no cure. Medications can help control the progression and ease the symptoms of MS. The scientific interest in the field of explainable artificial intelligence (AI) comes to the surface and aims to assist computer-aided diagnostic systems to be established in medical use by providing understandable and transparent information to the experts. The objective of this study was to present different learning methods of explainable AI models in the assessment of MS disease based on clinical data and brain magnetic resonance imaging (MRI) lesion texture features and compare them by focusing on the main findings. The learning methods used machine learning and argumentation theory to differentiate subjects with relapsing-remitting MS (RRMS) from progressive MS (PMS) subjects and provide explanations. The results showed that the different learning methods achieved a high accuracy of 99% and gave similar explanations as they extracted the same set of rules. It is hoped that the proposed methodology could lead to personalized treatment in the management of MS disease.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hone, L., Giovannoni, G., Dobson, R., Jacobs, B.M.: Predicting multiple sclerosis: challenges and opportunities. Front. Neurol. 12, 1–8 (2022)
Dobson, R., Giovannoni, G.: Multiple sclerosis-a review. Eur. J. Neurol. 26, 27–40 (2019)
Linardatos, P., Papastefanopoulos, V., Kotsiantis, S.: Explainable AI: a review of machine learning interpretability methods. Entropy 23, 1–45 (2021)
Kurtzke, J.F.: Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 33(11), 1444–1452 (1983)
Loizou, C.P., Petroudi, S., Seimenis, I., Pantziaris, M., Pattichis, C.S.: Quantitative texture analysis of brain white matter lesions derived from T2-weighted MR images in MS patients with clinically isolated syndrome. J. Neuroradiol. 42(2), 99–114 (2015)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: Synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V.: Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 108–122 (2013)
Lal, G.R., Chen, X., Mithal, V.: TE2Rules: extracting rule lists from tree ensembles, pp. 1–17 (2022)
Kakas, A.C., Moraitis, P., Spanoudakis, N.I.: GORGIAS: applying argumentation. Argument Comput. 10, 55–81 (2019)
Prentzas, N., Gavrielidou, A., Neophytou, M., Kakas, A.: Argumentation-based Explainable Machine Learning (ArgEML): a real-life use case on gynecological cancer. In: CEUR Workshop Proceedings, vol. 3208 (2022)
Prentzas, N., Nicolaides, A., Kyriacou, E., Kakas, A., Pattichis, C.: Integrating machine learning with symbolic reasoning to build an explainable ai model for stroke prediction. In: Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019, pp. 817–821. Institute of Electrical and Electronics Engineers Inc. (2019)
Prentzas, N., Pattichis, C., Kakas, A.: Explainable machine learning via argumentation. In: Communications in Computer and Information Science. Springer (2023)
Deng, H.: Interpreting tree ensembles with inTrees. Int. J. Data Sci. Anal. 7(4), 277–287 (2018). https://doi.org/10.1007/s41060-018-0144-8
Nicolaou, A., Loizou, C.P., Pantzaris, M., Kakas, A., Pattichis, C.S.: Rule extraction in the assessment of brain mri lesions in multiple sclerosis: preliminary findings. In: Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M. (eds.) CAIP 2021. LNCS, vol. 13052, pp. 277–286. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-89128-2_27
Nicolaou, A., et al.: An explainable artificial intelligence model in the assessment of brain MRI lesions in multiple sclerosis using amplitude modulation – frequency modulation multi-scale feature sets. In: 24th International Conference on Digital Signal Processing (DSP), pp. 1–4. Rhodes, Greece (2023)
Basu, S., Munafo, A., Ben-Amor, A.F., Roy, S., Girard, P., Terranova, N.: Predicting disease activity in patients with multiple sclerosis: an explainable machine-learning approach in the Mavenclad trials. CPT Pharm. Syst. Pharmacol. 11, 843–853 (2022)
Olatunji, S.O., Alsheikh, N., Alnajrani, L., Alanazy, A., Almusairii, M., et al.: Comprehensible machine-learning-based models for the pre-emptive diagnosis of multiple sclerosis using clinical data: a retrospective study in the Eastern province of Saudi Arabia. Int. J. Environ. Res. Public Health 20 (2023)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nicolaou, A., Prentzas, N., Loizou, C.P., Pantzaris, M., Kakas, A., Pattichis, C.S. (2023). A Comparative Study of Explainable AI models in the Assessment of Multiple Sclerosis. In: Tsapatsoulis, N., et al. Computer Analysis of Images and Patterns. CAIP 2023. Lecture Notes in Computer Science, vol 14185. Springer, Cham. https://doi.org/10.1007/978-3-031-44240-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-44240-7_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-44239-1
Online ISBN: 978-3-031-44240-7
eBook Packages: Computer ScienceComputer Science (R0)