基于SMOTE的深度网络与自适应提升烟尘技术用于2型糖尿病的检测和分类

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Phani Kumar Immadisetty, C. Rajabhushanam
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引用次数: 0

摘要

2 型糖尿病(T2D)是一种因胰腺胰岛素分泌不足而导致血糖水平异常升高的长期疾病。然而,这类疾病的检测和分类非常具有挑战性,需要有效的技术来学习 T2D 特征。因此,本研究提出使用一种基于混合深度学习的新型技术,通过有效学习疾病属性来自动检测和分类 T2D。首先,引入缺失值估算和基于归一化的预处理阶段来提高数据质量。然后,使用自适应助推燕鸥优化(Adap-BSTO)方法来选择最佳特征,同时最大限度地降低复杂性。之后,使用合成少数群体过度采样技术(SMOTE)来验证数据库类别的均匀分布。最后,提出了基于深度卷积注意力的双向循环神经网络(DCA-BiRNN)技术,用于准确检测和分类是否患有 T2D 疾病。该研究是通过 Python 平台进行的,并利用了两个公开的 PIMA 印度和 HFD 数据库。准确度、NPV、kappa 分数、Mathew 相关系数(MCC)、误诊率(FDR)和时间复杂性等评估指标都在研究之列,并与之前的研究进行了比较。对于 PIMA 印度数据集,建议方法的总体准确率为 99.6%,FDR 为 0.0038,kappa 为 99.24%,NPV 为 99.6%。对于 HFD 数据集,建议的方法分别获得了 99.5% 的总体准确率、0.0052 的 FDR、99% 的 kappa 和 99.4% 的 NPV。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

SMOTE-Based deep network with adaptive boosted sooty for the detection and classification of type 2 diabetes mellitus

SMOTE-Based deep network with adaptive boosted sooty for the detection and classification of type 2 diabetes mellitus

Type 2 diabetes (T2D) is a prolonged disease caused by abnormal rise in glucose levels due to poor insulin production in the pancreas. However, the detection and classification of this type of disease is very challenging and requires effective techniques for learning the T2D features. Therefore, this study proposes the use of a novel hybridized deep learning-based technique to automatically detect and categorize T2D by effectively learning disease attributes. First, missing value imputation and a normalization-based pre-processing phase are introduced to improve the quality of the data. The Adaptive Boosted Sooty Tern Optimization (Adap-BSTO) approach is then used to select the best features while minimizing complexity. After that, the Synthetic Minority Oversampling Technique (SMOTE) is used to verify that the database classes are evenly distributed. Finally, the Deep Convolutional Attention-based Bidirectional Recurrent Neural Network (DCA-BiRNN) technique is proposed to detect and classify the presence and absence of T2D disease accurately. The proposed study is instigated via the Python platform, and two publicly available PIMA Indian and HFD databases are utilized in this study. Accuracy, NPV, kappa score, Mathew's correlation coefficient (MCC), false discovery rate (FDR), and time complexity are among the assessment metrics examined and compared to prior research. For the PIMA Indian dataset, the proposed method obtains an overall accuracy of 99.6%, FDR of 0.0038, kappa of 99.24%, and NPV of 99.6%. For the HFD dataset, the proposed method acquires an overall accuracy of 99.5%, FDR of 0.0052, kappa of 99%, and NPV of 99.4%, respectively.

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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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