混合糖尿病风险预测模型 XGB-ILSO-1DCNN

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Huifang Feng, Yanan Hui
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引用次数: 0

摘要

准确预测糖尿病风险对于早期干预和预防至关重要。为了实现精准的糖尿病风险预测,我们提出了一种混合糖尿病风险预测模型--XGB-ILSO-1DCNN,它结合了极梯度提升(XGBoost)算法、改进狮群优化算法和深度学习模型 1DCNN。首先,基于原始数据训练 XGBoost,并将基于 XGBoost 的预测结果视为新特征,与原始特征串联形成新特征集。然后,我们介绍了一种名为 ILSO-1DCNN 的混合方法,它基于改进的狮群优化(ILSO)和一维卷积神经网络(1DCNN)。该方法是针对糖尿病风险预测而提出的。ILSO-1DCNN 算法利用 ILSO 的优化功能自动确定 1DCNN 网络的超参数。最后,我们在 PIMA 数据集上进行了综合实验,并将我们的模型与基线模型进行了比较。实验结果不仅证明了我们的模型在各种评估标准中都具有卓越的预测性能,还突出了它的高效性和低复杂性。本研究介绍了一种新颖有效的糖尿病风险预测方法,使其成为糖尿病患者护理临床分析的重要工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A hybrid diabetes risk prediction model XGB-ILSO-1DCNN

A hybrid diabetes risk prediction model XGB-ILSO-1DCNN

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.

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来源期刊
Multimedia Tools and Applications
Multimedia Tools and Applications 工程技术-工程:电子与电气
CiteScore
7.20
自引率
16.70%
发文量
2439
审稿时长
9.2 months
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
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