Demystifying multiple sclerosis diagnosis using interpretable and understandable artificial intelligence
Multiple sclerosis (MS) is a dangerous illness that strikes the central nervous system. The body’s immune system attacks myelin (an entity above the nerves) and impairs brain-to-body communication. To date, it is not possible to cure MS. However, symptoms can be managed, and treatments can be provid...
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| Format: | Article |
| Language: | English |
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De Gruyter
2024-12-01
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| Series: | Journal of Intelligent Systems |
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| Online Access: | https://doi.org/10.1515/jisys-2024-0077 |
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| author | Chadaga Krishnaraj Khanna Varada Vivek Prabhu Srikanth Sampathila Niranjana Chadaga Rajagopala Palkar Anisha |
| author_facet | Chadaga Krishnaraj Khanna Varada Vivek Prabhu Srikanth Sampathila Niranjana Chadaga Rajagopala Palkar Anisha |
| author_sort | Chadaga Krishnaraj |
| collection | DOAJ |
| description | Multiple sclerosis (MS) is a dangerous illness that strikes the central nervous system. The body’s immune system attacks myelin (an entity above the nerves) and impairs brain-to-body communication. To date, it is not possible to cure MS. However, symptoms can be managed, and treatments can be provided if the disease is diagnosed early. Hence, supervised machine learning (ML) algorithms and several hyperparameter tuning techniques, including Bayesian optimization, have been utilized in this study to predict MS in patients. Descriptive and inferential statistical analysis has been conducted before training the classifiers. The most essential markers were chosen using a technique called mutual information. Among the search techniques, the Bayesian optimization search technique prevailed to be pre-eminent, with an accuracy of 89%. To comprehend the diagnosis generated by the ML classifiers, four techniques of explainable artificial intelligence were utilized. According to them, the crucial attributes are periventricular magnetic resonance imaging (MRI), infratentorial MRI, oligoclonal bands, spinal cord MRI, breastfeeding, varicella disease, and initial symptoms. The models could be deployed in various medical facilities to detect MS in patients. The doctors could also use this framework to get a second opinion regarding the diagnosis. |
| format | Article |
| id | doaj-art-88100d775d7b40cdbee3d7f1fafac6bd |
| institution | OA Journals |
| issn | 2191-026X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj-art-88100d775d7b40cdbee3d7f1fafac6bd2025-08-20T02:36:20ZengDe GruyterJournal of Intelligent Systems2191-026X2024-12-013311152710.1515/jisys-2024-0077Demystifying multiple sclerosis diagnosis using interpretable and understandable artificial intelligenceChadaga Krishnaraj0Khanna Varada Vivek1Prabhu Srikanth2Sampathila Niranjana3Chadaga Rajagopala4Palkar Anisha5Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, IndiaDepartment of Biostatistics, Yale School of Public Health, Yale University, New Haven, 06510, CT, United StatesDepartment of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, IndiaDepartment of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, IndiaDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, IndiaDepartment of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, IndiaMultiple sclerosis (MS) is a dangerous illness that strikes the central nervous system. The body’s immune system attacks myelin (an entity above the nerves) and impairs brain-to-body communication. To date, it is not possible to cure MS. However, symptoms can be managed, and treatments can be provided if the disease is diagnosed early. Hence, supervised machine learning (ML) algorithms and several hyperparameter tuning techniques, including Bayesian optimization, have been utilized in this study to predict MS in patients. Descriptive and inferential statistical analysis has been conducted before training the classifiers. The most essential markers were chosen using a technique called mutual information. Among the search techniques, the Bayesian optimization search technique prevailed to be pre-eminent, with an accuracy of 89%. To comprehend the diagnosis generated by the ML classifiers, four techniques of explainable artificial intelligence were utilized. According to them, the crucial attributes are periventricular magnetic resonance imaging (MRI), infratentorial MRI, oligoclonal bands, spinal cord MRI, breastfeeding, varicella disease, and initial symptoms. The models could be deployed in various medical facilities to detect MS in patients. The doctors could also use this framework to get a second opinion regarding the diagnosis.https://doi.org/10.1515/jisys-2024-0077bayesian optimizationexplainable artificial intelligencehyperparameter tuning techniquesmachine learningmultiple sclerosis |
| spellingShingle | Chadaga Krishnaraj Khanna Varada Vivek Prabhu Srikanth Sampathila Niranjana Chadaga Rajagopala Palkar Anisha Demystifying multiple sclerosis diagnosis using interpretable and understandable artificial intelligence Journal of Intelligent Systems bayesian optimization explainable artificial intelligence hyperparameter tuning techniques machine learning multiple sclerosis |
| title | Demystifying multiple sclerosis diagnosis using interpretable and understandable artificial intelligence |
| title_full | Demystifying multiple sclerosis diagnosis using interpretable and understandable artificial intelligence |
| title_fullStr | Demystifying multiple sclerosis diagnosis using interpretable and understandable artificial intelligence |
| title_full_unstemmed | Demystifying multiple sclerosis diagnosis using interpretable and understandable artificial intelligence |
| title_short | Demystifying multiple sclerosis diagnosis using interpretable and understandable artificial intelligence |
| title_sort | demystifying multiple sclerosis diagnosis using interpretable and understandable artificial intelligence |
| topic | bayesian optimization explainable artificial intelligence hyperparameter tuning techniques machine learning multiple sclerosis |
| url | https://doi.org/10.1515/jisys-2024-0077 |
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