A Proposed Explainable Artificial Intelligence-Based Machine Learning Model for Discriminative Metabolites for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Abstract:

Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex and debilitating disease with a significant global prevalence of over 65 million individuals. It affects various systems, including the immune, neurological, gastrointestinal, and circulatory systems. Studies have shown abnormalities in immune cell types, increased inflammatory cytokines, and brain abnormalities. Further research is needed to identify consistent biomarkers and develop targeted therapies. A multidisciplinary approach is essential for diagnosing, treating, and managing this complex disease.

The current study aims at employing explainable artificial intelligence (XAI) and machine learning (ML) techniques to identify discriminative metabolites for ME/CFS.

Material and Methods: The present study used a metabolomics dataset of CFS patients and healthy controls, including 26 healthy controls and 26 ME/CFS patients aged 22-72. The dataset encapsulated 768 metabolites, classified into nine metabolic super-pathways: amino acids, carbohydrates, cofactors, vitamins, energy, lipids, nucleotides, peptides, and xenobiotics.

Random forest-based feature selection and Bayesian Approach based-hyperparameter optimization were implemented on the target data. Four different ML algorithms [Gaussian Naive Bayes (GNB), Gradient Boosting Classifier (GBC), Logistic regression (LR) and Random Forest Classifier (RFC)] were used to classify individuals as ME/CFS patients and healthy individuals. XAI approaches were applied to clinically explain the prediction decisions of the optimum model. Performance evaluation was performed using the indices of accuracy, precision, recall, F1 score, Brier score, and AUC.

Results: The metabolomics of C-glycosyltryptophan, oleoylcholine, cortisone, and 3-hydroxydecanoate were determined to be crucial for ME/CFS diagnosis.

The RFC learning model outperformed GNB, GBC, and LR in ME/CFS prediction using the 1000 iteration bootstrapping method, achieving 98% accuracy, precision, recall, F1 score, 0.01 Brier score, and 99% AUC.

Conclusion: RFC model proposed in this study correctly classified and evaluated ME/CFS patients through the selected biomarker candidate metabolites. The methodology combining ML and XAI can provide a clear interpretation of risk estimation for ME/CFS, helping physicians intuitively understand the impact of key metabolomics features in the model.

Source: Yagin, F.H., Alkhateeb, A., Raza, A., Samee, N.A., Mahmoud, N.F., Colak, C., & Yagin, B. (2023). A Proposed Explainable Artificial Intelligence-Based Machine Learning Model for Discriminative Metabolites for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Preprints. https://doi.org/10.20944/preprints202307.1585.v1 https://www.preprints.org/manuscript/202307.1585/v1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10706650/ (Full text of completed study)

A computational analysis of Canale-Smith syndrome: chronic lymphadenopathy simulating malignant lymphoma

Abstract:

OBJECTIVE: The objective of this study was to simulate changes in the human T cell system representing Canale-Smith syndrome using a dynamic computer model of T cell development and comparing with available human data.

STUDY DESIGN: Physiological stepwise maturation and function of T lymphocytes in the computer model is altered by introducing functional disturbances following lymphotropic virus infection. In the present model, acute and chronic persistent infection with the human herpesvirus-6 (HHV-6) was simulated, and ensuing changes in T cell populations were compared with those measured in human patients.

RESULTS: Using our computer model we previously found that simulated acute HHV-6 infection produced T cell computer data, which resembled an infectious mononucleosis-like disease in patients. Simulated chronic persistent infection, instead, resulted in variable cell changes comparing well to patients with chronic fatigue syndrome. In one setting, however, persistent immature lymphocytosis was observed similar to what initial has been described in this journal as Canale-Smith syndrome.

CONCLUSION: Using a computer model developed by us we were able to produce simulations that resemble the immune system features of Canale-Smith syndrome. Further understanding of these simulation results may possibly guide future investigations into this disorder.

 

Source: Krueger GR, Brandt ME, Wang G, Berthold F, Buja LM. A computational analysis of Canale-Smith syndrome: chronic lymphadenopathy simulating malignant lymphoma. Anticancer Res. 2002 Jul-Aug;22(4):2365-71. http://www.ncbi.nlm.nih.gov/pubmed/12174928