Prediction of myalgic chronic fatigue syndrome disorder with machine learning approach

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  • Fatma Hilal Yagin
  • Badicu Georgian Department of Physical Education and Special Motricity, Transilvania University of Brasov, 500068 Brasov, Romania



Metabolomics, machine learning, random forest, gaussian naive bayes


Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex disorder characterized by unexplained fatigue, post-exertional malaise, unrefreshing sleep, and cognitive impairment or orthostatic intolerance. Due to the absence of a recognized laboratory diagnostic test, diagnosis relies on patient history and physical examination. This study aimed to identify significant metabolomic markers and employ machine learning techniques for the classification of ME/CFS. Utilizing open-access metabolomics data from 26 ME/CFS patients and 26 controls, we implemented a comprehensive data preprocessing and modeling framework. Feature selection was performed using Random Forest, and data normalization was achieved through standardization. A Gaussian Naive Bayes model was trained and validated using 5-fold cross-validation. The model exhibited an accuracy of 0.786, sensitivity of 0.952, specificity of 0.619, and an F1 score of 0.816. These results indicate a high efficacy in identifying positive cases of ME/CFS.




How to Cite

Yagin , F. H. ., & Georgian, B. (2024). Prediction of myalgic chronic fatigue syndrome disorder with machine learning approach. Journal of Exercise Science & Physical Activity Reviews, 2(1), 97–103.



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