Machine Learning-Driven Metabolomic Biomarker Discovery for PCOS: An Interpretable Approach Using Random Forest and SHAP.
Aim: This study aimed to predict Polycystic Ovary Syndrome (PCOS) using follicular fluid metabolomic data and the Random Forest algorithm, and to interpret the contributions of the most influential metabolites using SHapley Additive exPlanations (SHAP) analysis. Material and Method: An untargeted me...
| Publicado en: | Medical Records Vol. 7; no. 3; pp. 763 - 768 |
|---|---|
| Autor principal: | |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Medical Records
2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188648565&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188648565 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 26874555 N21P jtl: Medical Records issn: 26874555 maglogo: N pubinfo: dt: 2025 vid: 7 iid: 3 pid: 71272 pub: Medical Records artinfo: ui: 188648565 188648565 188648565 10.37990/medr.1718952 188648565 ppf: 763 ppct: 5 formats: tig: atl: Machine Learning-Driven Metabolomic Biomarker Discovery for PCOS: An Interpretable Approach Using Random Forest and SHAP. aug: au: Yasar, Seyma affil: İnönü University, Faculty of Medicine, Department of Biostatistics and Medical Informatics, Malatya, Türkiye sug: subj: Polyendocrine Metabolic Ovarian Syndrome Risk Factors Risk Assessment Methods Ovarian Follicle Metabolism Interstitial Fluid Metabolism Metabolites Biological Markers Diagnosis, Computer Assisted Machine Learning Algorithms Evaluation Prediction Models Evaluation Human Female Adult Validation Studies Validity Sensitivity and Specificity Histidine Glutamine Tyrosine Amino Acids Metabolism Descriptive Statistics T-Tests Mann-Whitney U Test Data Analysis Software Adult: 19-44 years Female ab: Aim: This study aimed to predict Polycystic Ovary Syndrome (PCOS) using follicular fluid metabolomic data and the Random Forest algorithm, and to interpret the contributions of the most influential metabolites using SHapley Additive exPlanations (SHAP) analysis. Material and Method: An untargeted metabolomic dataset of follicular fluid from 35 PCOS patients and 37 age-matched controls was utilized. The dataset was partitioned into 70% training and 30% testing subsets using stratified sampling. A Random Forest algorithm was employed, with hyperparameter optimization performed using RandomizedSearchCV. Model performance was evaluated using accuracy, sensitivity, specificity, F1 score, balanced accuracy, and Brier score. SHAP analysis was then applied to interpret the model's predictions and identify key contributing metabolites. Results: The Random Forest model achieved robust classification performance, with an accuracy of 0.86, sensitivity of 0.82, specificity of 0.91, F1 score of 0.86, balanced accuracy of 0.85, and a Brier score of 0.13. SHAP analysis identified L-Histidine, L-Glutamine, and L-Tyrosine as the top three most influential metabolites. Specifically, decreased levels of L-Histidine and L-Tyrosine, and elevated levels of L-Glutamine, were associated with an increased risk of PCOS. Conclusion: Our findings demonstrate the potential of integrating machine learning with explainable AI to accurately predict PCOS based on metabolomic profiles. The identified metabolites, particularly alterations in amino acid metabolism, offer novel insights into the metabolic underpinnings of PCOS and highlight their promise as diagnostic biomarkers, paving the way for more precise and interpretable diagnostic strategies. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|