A diabetes prediction model based on Boruta feature selection and ensemble learning

BMC Bioinformatics

Research

Yinbo Xin¹  Suli Li¹  Hongfang Zhou² 

[1] School of Computer Science and Engineering, Xi’an University of Technology, 710048, Xi’an, China;School of Computer Science and Engineering, Xi’an University of Technology, 710048, Xi’an, China;Shaanxi Key Laboratory of Network Computing and Security Technology, 710048, Xi’an, China;

关键词: Diabetes detection;    Machine learning;    Boruta feature selection;    K-Means++;    Ensemble learning;   

DOI  :  10.1186/s12859-023-05300-5

 received in 2022-07-15, accepted in 2023-04-21,  发布年份 2023

来源: Springer

【 摘 要 】

Background and objectiveAs a common chronic disease, diabetes is called the “second killer” among modern diseases. Currently, there is no medical cure for diabetes. We can only rely on medication for auxiliary treatment. However, many diabetic patients still die each year. In addition, a considerable number of people do not pay attention to their physical health or opt out of treatment due to lack of money, which eventually leads to various complications. Therefore, diagnosing diabetes at an early stage and intervening early is necessary; thus, developing an early detection method for diabetes is essential.MethodsIn this study, a diabetes prediction model based on Boruta feature selection and ensemble learning is proposed. The model contains the use of Boruta feature selection, the extraction of salient features from datasets, the use of the K-Means++ algorithm for unsupervised clustering of data and stacking of an ensemble learning method for classification. It has been validated on a diabetes dataset.ResultsThe experiments were performed on the PIMA Indian diabetes dataset. The model was evaluated by accuracy, precision and F1 index. The obtained results show that the accuracy rate of the model reaches 98% and achieves good results.ConclusionCompared with other diabetes prediction models, this model achieved better results, and the obtained results indicate that this model is superior to other models in diabetes prediction and has better performance.

【 授权许可】

CC BY   
© The Author(s) 2023

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