Integrated bagging-RF learning model for diabetes diagnosis in middle-aged and elderly population.

ABSTRACT: As the population ages, the increase in the number of middle-aged and older adults with diabetes poses new challenges to the allocation of resources in the healthcare system. Developing accurate diabetes prediction models is a critical public health strategy to improve the efficient use of healthcare resources and ensure timely and effective treatment. In order to improve the identification of diabetes in middle-aged and older patients, a Bagging-RF model is proposed. In the study, two diabetes datasets on Kaggle were first preprocessed, including unique heat coding, outlier removal, and age screening, after which the data were categorized into three age groups, 50-60, 60-70, and 70-80, and balanced using the SMOTE technique. Then, the machine learning classifiers were trained using the Ba

SUBMITTER: Shi Y 

PROVIDER: S-EPMC11623014 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature