DiabPrednet:基于注意的长短期记忆的糖尿病预测模型,该模型具有最优加权特征融合机制

IF 1.3 Q4 ENGINEERING, BIOMEDICAL
S. Nagendiran, S. Rohini, P. Jagadeesan, S. Shankari, R. Harini
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引用次数: 0

摘要

摘要机器学习是一种自动从经验中学习的计算机技术,可以提高产生更精确的糖尿病预测的有效性。然而,训练机器学习网络需要庞大、包容、高质量的数据集。在这项研究工作中,基于注意力的方法被设计用于预测受影响个体的糖尿病。首先,将收集到的糖尿病数据进行数据清洗,得到用于预测任务的无噪声数据。在这里,提取的特征集1是从Auto encoder中提取的,提取的特征集2是从一维卷积神经网络(1D-CNN)中提取的。将提取的两组特征以加权特征融合的自适应方式进行融合。在这里,通过增强寻径算法(Enhanced Path Finder Algorithm, EPFA)优化所选特征的权重,以获得更准确的结果。在糖尿病预测阶段,采用加权融合特征,利用改进PFA优化结构的基于注意的长短期记忆(ALSTM)来预测受影响者的糖尿病。在整个结果分析中,设计的方法达到95%的准确度和92%的精密度。最后,将本文提出的预测方法与现有的预测方法进行对比分析,以展示其有效性能。关键词:糖尿病预测、自编码器、一维卷积神经网络、基于注意的长短期记忆组件、增强型寻径器算法披露声明作者未报告潜在利益冲突。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DiabPrednet: development of attention-based long short-term memory-based diabetes prediction model with optimal weighted feature fusion mechanism
ABSTRACTMachine learning is a computer technique that automatically learns from experience and enhances the effectiveness of producing more precise diabetes predictions. However, large, inclusive, high-quality datasets are needed for training the machine learning networks. In this research work, attention-based approaches are designed for predicting diabetes in the affected individuals. Initially, the collected diabetes data is given into the data cleaning to get noise-free data for the prediction task. Here, extracted feature set 1 is extracted from the Auto encoder, and extracted feature set 2 is extracted from the 1-Dimensional Convolutional Neural Network (1D-CNN). These two sets of extracted features are fused in the adaptive way that is weighted feature fusion. Here, the weight of the selected features is optimized by an Enhanced Path Finder Algorithm (EPFA) to get more accurate results. The weighted fused features are employed for the diabetes prediction phase, in which the developed Attention-based Long Short Term Memory (ALSTM) with architecture optimization by improved PFA for predicting diabetes in affected one. Throughout the result analysis, the designed method attains 95% accuracy and 92%precision rate. Finally, the analysis is made by the proposed and existing prediction methods to showcase the effective performance.KEYWORDS: Diabetes predictionautoencoder1-dimensional convolutional neural networkattention-based long short term memory componentenhanced path finder algorithm Disclosure statementNo potential conflict of interest was reported by the author(s).
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来源期刊
CiteScore
2.80
自引率
6.20%
发文量
102
期刊介绍: Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization is an international journal whose main goals are to promote solutions of excellence for both imaging and visualization of biomedical data, and establish links among researchers, clinicians, the medical technology sector and end-users. The journal provides a comprehensive forum for discussion of the current state-of-the-art in the scientific fields related to imaging and visualization, including, but not limited to: Applications of Imaging and Visualization Computational Bio- imaging and Visualization Computer Aided Diagnosis, Surgery, Therapy and Treatment Data Processing and Analysis Devices for Imaging and Visualization Grid and High Performance Computing for Imaging and Visualization Human Perception in Imaging and Visualization Image Processing and Analysis Image-based Geometric Modelling Imaging and Visualization in Biomechanics Imaging and Visualization in Biomedical Engineering Medical Clinics Medical Imaging and Visualization Multi-modal Imaging and Visualization Multiscale Imaging and Visualization Scientific Visualization Software Development for Imaging and Visualization Telemedicine Systems and Applications Virtual Reality Visual Data Mining and Knowledge Discovery.
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