{"title":"A hybrid diabetes risk prediction model XGB-ILSO-1DCNN","authors":"Huifang Feng, Yanan Hui","doi":"10.1007/s11042-024-20155-5","DOIUrl":null,"url":null,"abstract":"<p>Accurately predicting the risk of diabetes is of paramount importance for early intervention and prevention. To achieve precise diabetes risk prediction, we propose a hybrid diabetes risk prediction model, XGB-ILSO-1DCNN, which combines the Extreme Gradient Boosting (XGBoost) algorithm, the Improved Lion Swarm Optimization algorithm, and the deep learning model 1DCNN. Firstly, an XGBoost is trained based on the raw data and the prediction result based on XGBoost is regarded as a new feature, concatenating it with the original features to form a new feature set. Then, we introduce a hybrid approach called ILSO-1DCNN, which is based on improved Lion Swarm Optimization (ILSO) and one-dimensional convolutional neural network (1DCNN). This approach is proposed for diabetes risk prediction. The ILSO-1DCNN algorithm utilizes the optimization capabilities of ILSO to automatically determine the hyperparameters of the 1DCNN network. Finally, we conducted comprehensive experiments on the PIMA dataset and compared our model with baseline models. The experimental results not only demonstrate our model's exceptional predictive performance across various evaluation criteria but also highlight its efficiency and low complexity. This study introduces a novel and effective diabetes risk prediction approach, making it a valuable tool for clinical analysis in the care of diabetic patients.</p>","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Multimedia Tools and Applications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11042-024-20155-5","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Accurately predicting the risk of diabetes is of paramount importance for early intervention and prevention. To achieve precise diabetes risk prediction, we propose a hybrid diabetes risk prediction model, XGB-ILSO-1DCNN, which combines the Extreme Gradient Boosting (XGBoost) algorithm, the Improved Lion Swarm Optimization algorithm, and the deep learning model 1DCNN. Firstly, an XGBoost is trained based on the raw data and the prediction result based on XGBoost is regarded as a new feature, concatenating it with the original features to form a new feature set. Then, we introduce a hybrid approach called ILSO-1DCNN, which is based on improved Lion Swarm Optimization (ILSO) and one-dimensional convolutional neural network (1DCNN). This approach is proposed for diabetes risk prediction. The ILSO-1DCNN algorithm utilizes the optimization capabilities of ILSO to automatically determine the hyperparameters of the 1DCNN network. Finally, we conducted comprehensive experiments on the PIMA dataset and compared our model with baseline models. The experimental results not only demonstrate our model's exceptional predictive performance across various evaluation criteria but also highlight its efficiency and low complexity. This study introduces a novel and effective diabetes risk prediction approach, making it a valuable tool for clinical analysis in the care of diabetic patients.
期刊介绍:
Multimedia Tools and Applications publishes original research articles on multimedia development and system support tools as well as case studies of multimedia applications. It also features experimental and survey articles. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed.
Specific areas of interest include:
- Multimedia Tools:
- Multimedia Applications:
- Prototype multimedia systems and platforms