Exploring Biomarker Relationships in Both Type 1 and Type 2 Diabetes Mellitus Through a Bayesian Network Analysis Approach

Autor:	Sun, Yuyang, Lei, Jingyu, Kosmas, Panagiotis
Rok vydání:	2024
Předmět:	Quantitative Biology - Quantitative Methods Computer Science - Artificial Intelligence Computer Science - Computational Engineering Finance and Science Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Understanding the complex relationships of biomarkers in diabetes is pivotal for advancing treatment strategies, a pressing need in diabetes research. This study applies Bayesian network structure learning to analyze the Shanghai Type 1 and Type 2 diabetes mellitus datasets, revealing complex relationships among key diabetes-related biomarkers. The constructed Bayesian network presented notable predictive accuracy, particularly for Type 2 diabetes mellitus, with root mean squared error (RMSE) of 18.23 mg/dL, as validated through leave-one-domain experiments and Clarke error grid analysis. This study not only elucidates the intricate dynamics of diabetes through a deeper understanding of biomarker interplay but also underscores the significant potential of integrating data-driven and knowledge-driven methodologies in the realm of personalized diabetes management. Such an approach paves the way for more custom and effective treatment strategies, marking a notable advancement in the field. Comment: Paper is accepted by EMBC 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.17090 Zobrazit plný text záznamu View this record from Arxiv