Prediction of myalgic chronic fatigue syndrome disorder with machine learning approach

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.