Machine learning classification meets migraine: recommendations for study evaluation
Abstract The integration of machine learning (ML) classification techniques into migraine research has offered new insights into the pathophysiology and classification of migraine types and subtypes. However, inconsistencies in study design, lack of methodological transparency, and the absence of ex...
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| Format: | Article |
| Language: | English |
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BMC
2024-12-01
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| Series: | The Journal of Headache and Pain |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s10194-024-01924-x |
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| _version_ | 1846112384227213312 |
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| author | Igor Petrušić Andrej Savić Katarina Mitrović Nebojša Bačanin Gabriele Sebastianelli Daniele Secci Gianluca Coppola |
| author_facet | Igor Petrušić Andrej Savić Katarina Mitrović Nebojša Bačanin Gabriele Sebastianelli Daniele Secci Gianluca Coppola |
| author_sort | Igor Petrušić |
| collection | DOAJ |
| description | Abstract The integration of machine learning (ML) classification techniques into migraine research has offered new insights into the pathophysiology and classification of migraine types and subtypes. However, inconsistencies in study design, lack of methodological transparency, and the absence of external validation limit the impact and reproducibility of such studies. This paper presents a framework of six essential recommendations for evaluating ML-based classification in migraine research: (1) group homogenization by clinical phenotype, attack frequency, comorbidity, therapy, and demographics; (2) defining adequate sample size; (3) quality control of collected and preprocessed data; (4) transparent training, testing, and performance evaluation of ML models, including strategies for data splitting, overfitting control, and feature selection; (5) interpretability of results with clinical relevance; and (6) open data and code sharing to facilitate reproducibility. These recommendations aim to balance the trade-off between model generalization and precision while encouraging collaborative standardization across the ML and headache communities. Furthermore, this framework intends to stimulate discussion toward forming a consortium to establish definitive guidelines for ML-based classification research in migraine field. |
| format | Article |
| id | doaj-art-f93ed33d770042b1939107d17f92b0f1 |
| institution | Kabale University |
| issn | 1129-2377 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | The Journal of Headache and Pain |
| spelling | doaj-art-f93ed33d770042b1939107d17f92b0f12024-12-22T12:38:00ZengBMCThe Journal of Headache and Pain1129-23772024-12-012511710.1186/s10194-024-01924-xMachine learning classification meets migraine: recommendations for study evaluationIgor Petrušić0Andrej Savić1Katarina Mitrović2Nebojša Bačanin3Gabriele Sebastianelli4Daniele Secci5Gianluca Coppola6Laboratory for Advanced Analysis of Neuroimages, Faculty of Physical Chemistry, University of BelgradeScience and Research Centre, School of Electrical Engineering, University of Belgrade, University of BelgradeDepartment of Information Technologies, Faculty of Technical Sciences Čačak, University of KragujevacDepartment of Informatics and Computing, Singidunum UniversityDepartment of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome Polo Pontino ICOTDepartment of Engineering and Architecture, University of ParmaDepartment of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome Polo Pontino ICOTAbstract The integration of machine learning (ML) classification techniques into migraine research has offered new insights into the pathophysiology and classification of migraine types and subtypes. However, inconsistencies in study design, lack of methodological transparency, and the absence of external validation limit the impact and reproducibility of such studies. This paper presents a framework of six essential recommendations for evaluating ML-based classification in migraine research: (1) group homogenization by clinical phenotype, attack frequency, comorbidity, therapy, and demographics; (2) defining adequate sample size; (3) quality control of collected and preprocessed data; (4) transparent training, testing, and performance evaluation of ML models, including strategies for data splitting, overfitting control, and feature selection; (5) interpretability of results with clinical relevance; and (6) open data and code sharing to facilitate reproducibility. These recommendations aim to balance the trade-off between model generalization and precision while encouraging collaborative standardization across the ML and headache communities. Furthermore, this framework intends to stimulate discussion toward forming a consortium to establish definitive guidelines for ML-based classification research in migraine field.https://doi.org/10.1186/s10194-024-01924-xBenchmarkMachine learning classification modelsData qualityModel interpretabilityModel reproducibilityMigraine types |
| spellingShingle | Igor Petrušić Andrej Savić Katarina Mitrović Nebojša Bačanin Gabriele Sebastianelli Daniele Secci Gianluca Coppola Machine learning classification meets migraine: recommendations for study evaluation The Journal of Headache and Pain Benchmark Machine learning classification models Data quality Model interpretability Model reproducibility Migraine types |
| title | Machine learning classification meets migraine: recommendations for study evaluation |
| title_full | Machine learning classification meets migraine: recommendations for study evaluation |
| title_fullStr | Machine learning classification meets migraine: recommendations for study evaluation |
| title_full_unstemmed | Machine learning classification meets migraine: recommendations for study evaluation |
| title_short | Machine learning classification meets migraine: recommendations for study evaluation |
| title_sort | machine learning classification meets migraine recommendations for study evaluation |
| topic | Benchmark Machine learning classification models Data quality Model interpretability Model reproducibility Migraine types |
| url | https://doi.org/10.1186/s10194-024-01924-x |
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