Using machine learning methods to investigate the role of volatile organic compounds in non-alcoholic fatty liver disease
Bibliographic record
Abstract
Aims: Approximately 25%-30% of the global population is affected by non-alcoholic fatty liver disease (NAFLD). This study aimed to explore whether NAFLD could be effectively detected using 341 volatile organic compounds (VOCs) via 10 machine learning (Mach-L) algorithms in a cohort of 1,501 individuals. Methods: Participants were selected from the Taiwan MJ cohort, which includes comprehensive demographic, biochemical, lifestyle, and VOCs data. NAFLD was diagnosed by experienced gastroenterologists. Exhaled breath samples were collected using a 1.0-L aluminum bag (late expiratory fraction) and analyzed with selected-ion flow-tube mass spectrometry. Ten Mach-L techniques were employed to evaluate two predictive models: Model 1 (demographic, lifestyle, and biochemical data), and Model 2 (Model 1 + VOCs), assessed using area under the receiver operating characteristic curve (AUC). Results: Subjects with NAFLD had significantly higher values for age, BMI, blood pressure, and other biomedical markers, except for eGFR and HDL-C. Key predictors of NAFLD included BMI, triglycerides (TG), uric acid (UA), fasting plasma glucose (FPG), γ-GT, gender, LDL-C, and sleep duration. The addition of VOCs to Model 1 improved the AUC from 0.722 ± 0.149 to 0.770 ± 0.264 (p < 0.001). Ten VOCs were identified as the most influential, in order of importance: 2-propanol, acetone, butyl 2-methylbutanoate, diethylethanolamine, urethane, β-caryophyllene, furfural, tridecane, 4-methyloctanoic acid, and (S)-2-methyl-1-butanol. Conclusion: Incorporating VOCs into traditional demographic, biochemical, and lifestyle data significantly enhanced the model's predictive performance. This suggests that VOCs may be associated with the underlying pathophysiology of NAFLD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".