Trial Report Prediction of myalgic chronic fatigue syndrome disorder with machine learning approach, 2024, Yagin & Georgian

Free fulltext:
https://e-jespar.com/index.php/jespar/article/view/25

Prediction of myalgic chronic fatigue syndrome disorder with machine learning approach

Authors

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

DOI:
https://doi.org/10.5281/zenodo.12601089

Keywords:
Metabolomics, machine learning, random forest, gaussian naive bayes

Abstract

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.


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. https://doi.org/10.5281/zenodo.12601089

 

Same author, and also has 26 ME/CFS and 26 controls, all female: An Explainable Artificial Intelligence Model Proposed for the Prediction of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and the Identification of Distinctive Metabolites, 2023, Yagin et al (Thread)

Source given in that paper for the data: Comprehensive Circulatory Metabolomics in ME/CFS Reveals Disrupted Metabolism of Acyl Lipids and Steroids, 2020, Germain et al (Thread)

 

Machine learning with n=26 will result in an overfitted model that likely won’t replicate elsewhere.