Prediction of myalgic chronic fatigue syndrome disorder with machine learning approach


Abstract views: 41 / PDF downloads: 50

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.

Downloads

Published

2024-07-01

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

Issue

Section

Original Articles

Most read articles by the same author(s)

1 2 > >>