Abstract
Background
Fibromyalgia (FM) is a multifaceted chronic pain disorder presenting with pain throughout the musculoskeletal system, alongside chronic fatigue,sleep disturbances, and cognitive impairments. Current diagnostic criteria primarily rely on symptom questionnaires, with no specific laboratory markers or definitive tests, making diagnosis subjective and often delayed. Moreover, the overlap with other central pain syndromes further complicates accurate diagnosis. Neuroimaging studies, particularly functional magnetic resonance imaging (fMRI), have shown promise in elucidating brain abnormalities underlying FM. However, translating these findings into reliable diagnostic tools remains challenging, especially given small sample sizes. Therefore, this study aims to enhance the diagnostic accuracy of FM based on fMRI data by developing an intelligent model. This model employs machine learning algorithms designed to perform effectively with limited dataset sizes and incorporates features extracted through graph theory methods. We analyzed fMRI data from 32 FM patients and 30 healthy controls. Graph theory metrics were extracted from 13 regions of the emotion regulation network. A genetic algorithm was employed to select the most effective features, which were then used to train multiple machine learning classifiers—including Support Vector Machine (SVM) with polynomial and Radial Basis Function (RBF) kernels, Random Forest, Gradient Boosting Machine, Adaptive Boosting, and SVM with Mixture Kernels—within a cross-validation framework to ensure robustness.
Result
The genetic algorithm selected 84 features as inputs for machine learning algorithms. The polynomial SVM achieved the highest overall accuracy, whereas the SVM with mixture kernels demonstrated superior sensitivity and AUC. Sensitivity and AUC were prioritized as the main criteria for model selection due to their clinical relevance in reducing false negatives.
Conclusion
The study identified the SVM with Mixture Kernels as the most effective model for distinguishing FM patients from healthy controls. This approach demonstrates promising potential for enhancing diagnostic accuracy and supporting psychological and psychiatric interventions.