Dual Multi Scale Attention Network Optimized With Archerfish Hunting Optimization Algorithm for Diabetics Prediction.

IF 2 3区 工程技术 Q2 ANATOMY & MORPHOLOGY
Helina Rajini Suresh, K Anita Davamani, Hemalatha Chandrasekaran, N Prabu Sankar
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引用次数: 0

Abstract

Diabetes is a chronic disease that occurs when the body cannot regulate blood sugar levels. Nowadays, the screening tests for diabetes are developed using multivariate regression methods. An increasing amount of data is automatically collected to provide an opportunity for creating challenging and accurate prediction modes that are updated constantly with the help of machine learning techniques. In this manuscript, a Dual Multi Scale Attention Network optimized with Archerfish Hunting Optimization Algorithm is proposed for Diabetes Prediction (DMSAN-AHO-DP). Here, the data is gathered through PIMA Indian Diabetes Dataset (PIDD). The collected data is fed towards the preprocessing to remove the noise of input data and improves the data quality by using Contrast Limited Adaptive Histogram Equalization Filtering (CLAHEF) method. Then the preprocessed data are fed to Multi-Level Haar Wavelet Features Fusion Network (MHWFFN) based feature extraction. Then the extracted data is supplied to the Dual Multi Scale Attention Network (DMSAN) for diabetic or non-diabetic classification. The hyper parameter of Dual Multi Scale Attention Network is tuned with Archerfish Hunting Optimization (AHO) algorithm, which classifies diabetic or non-diabetic accurately. The proposed DMSAN-AHO-DP technique is implemented in Python. The efficacy of the DMSAN-AHO-DP approach is examined with some metrics, like Accuracy, F-scores, Sensitivity, Specificity, Precision, Recall, Computational time. The DMSAN-AHO-DP technique achieves 23.52%, 36.12%, 31.12% higher accuracy and 16.05%, 21.14%, 31.02% lesser error rate compared with existing models: Enhanced Deep Neural Network based Model for Diabetes Prediction (EDNN-DP), Indian PIMA Dataset using Deep Learning for Diabetes Prediction (ANN-DP), and Enhanced Support Vector Machine with Deep Neural Network Learning strategies for Diabetes Prediction (SVM-DNN-DP).

基于射水鱼狩猎优化算法的双多尺度注意网络糖尿病预测。
糖尿病是一种慢性疾病,当身体无法调节血糖水平时就会发生。目前,糖尿病筛查试验主要采用多元回归方法。越来越多的数据被自动收集,为创建具有挑战性和准确的预测模式提供了机会,这些预测模式在机器学习技术的帮助下不断更新。本文提出了一种基于射水鱼狩猎优化算法优化的双多尺度注意力网络用于糖尿病预测(DMSAN-AHO-DP)。这里,数据是通过PIMA印度糖尿病数据集(PIDD)收集的。采集到的数据进行预处理,采用对比度有限自适应直方图均衡化滤波(CLAHEF)方法去除输入数据中的噪声,提高数据质量。然后将预处理后的数据送入基于多级Haar小波特征融合网络(MHWFFN)的特征提取中。然后将提取的数据提供给双多尺度注意网络(DMSAN)进行糖尿病或非糖尿病分类。采用射水鱼狩猎优化(AHO)算法对双多尺度注意力网络的超参数进行调整,实现了糖尿病和非糖尿病的准确分类。提出的DMSAN-AHO-DP技术在Python中实现。DMSAN-AHO-DP方法的有效性通过一些指标进行检验,如准确性,f分数,灵敏度,特异性,精度,召回率,计算时间。与现有的基于深度神经网络的增强型糖尿病预测模型(EDNN-DP)、基于深度学习的印度PIMA数据集(ANN-DP)和基于深度神经网络学习策略的增强型支持向量机糖尿病预测模型(SVM-DNN-DP)相比,DMSAN-AHO-DP技术的准确率分别提高了23.52%、36.12%、31.12%,错误率降低了16.05%、21.14%、31.02%。
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来源期刊
Microscopy Research and Technique
Microscopy Research and Technique 医学-解剖学与形态学
CiteScore
5.30
自引率
20.00%
发文量
233
审稿时长
4.7 months
期刊介绍: Microscopy Research and Technique (MRT) publishes articles on all aspects of advanced microscopy original architecture and methodologies with applications in the biological, clinical, chemical, and materials sciences. Original basic and applied research as well as technical papers dealing with the various subsets of microscopy are encouraged. MRT is the right form for those developing new microscopy methods or using the microscope to answer key questions in basic and applied research.
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