Enhancing type 2 diabetes mellitus prediction by integrating metabolomics and tree-based boosting approaches

Type 2 diabetes mellitus (T2DM) is a global health problem characterized by insulin resistance and hyperglycemia. Early detection and accurate prediction of T2DM is crucial for effective management and prevention. This study explores the integration of machine learning (ML) and explainable artificia...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Frontiers in endocrinology (Lausanne) Ročník 15; s. 1444282
Hlavní autoři: Arslan, Ahmet Kadir, Yagin, Fatma Hilal, Algarni, Abdulmohsen, Karaaslan, Erol, Al-Hashem, Fahaid, Ardigò, Luca Paolo
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland Frontiers Media S.A 11.11.2024
Témata:
Adult
Aged
Algorithms
Artificial Intelligence
biomarkers
Biomarkers - metabolism
Case-Control Studies
Diabetes Mellitus, Type 2 - metabolism
Endocrinology
explainable artificial intelligence
Female
Humans
Machine Learning
Male
metabolomics
Metabolomics - methods
Middle Aged
predictive modeling
Prognosis
type 2 diabetes
explainable artificial intelligence
type 2 diabetes
machine learning
metabolomics
biomarkers
predictive modeling
ISSN:1664-2392, 1664-2392
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Type 2 diabetes mellitus (T2DM) is a global health problem characterized by insulin resistance and hyperglycemia. Early detection and accurate prediction of T2DM is crucial for effective management and prevention. This study explores the integration of machine learning (ML) and explainable artificial intelligence (XAI) approaches based on metabolomics panel data to identify biomarkers and develop predictive models for T2DM. Metabolomics data from T2DM (n = 31) and healthy controls (n = 34) were analyzed for biomarker discovery (mostly amino acids, fatty acids, and purines) and T2DM prediction. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) regression to enhance the model's accuracy and interpretability. Advanced three tree-based ML algorithms (KTBoost: Kernel-Tree Boosting; XGBoost: eXtreme Gradient Boosting; NGBoost: Natural Gradient Boosting) were employed to predict T2DM using these biomarkers. The SHapley Additive exPlanations (SHAP) method was used to explain the effects of metabolomics biomarkers on the prediction of the model. The study identified multiple metabolites associated with T2DM, where LASSO feature selection highlighted important biomarkers. KTBoost [Accuracy: 0.938; CI: (0.880-0.997), Sensitivity: 0.971; CI: (0.847-0.999), Area under the Curve (AUC): 0.965; CI: (0.937-0.994)] demonstrated its effectiveness in using complex metabolomics data for T2DM prediction and achieved better performance than other models. According to KTBoost's SHAP, high levels of phenylactate (pla) and taurine metabolites, as well as low concentrations of cysteine, laspartate, and lcysteate, are strongly associated with the presence of T2DM. The integration of metabolomics profiling and XAI offers a promising approach to predicting T2DM. The use of tree-based algorithms, in particular KTBoost, provides a robust framework for analyzing complex datasets and improves the prediction accuracy of T2DM onset. Future research should focus on validating these biomarkers and models in larger, more diverse populations to solidify their clinical utility.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Edited by: Prasanna Santhanam, Johns Hopkins University, United States
Reviewed by: Jian Hua Huang, Hunan University of Chinese Medicine, China
Lin-Ching Chang, The Catholic University of America, United States
ISSN:1664-2392
1664-2392
DOI:10.3389/fendo.2024.1444282