Type 2 diabetes data classification using stacked autoencoders in deep neural networks

Kannadasan, K; Edla, Damodar Reddy; Kuppili, Venkatanareshbabu

doi:10.1016/j.cegh.2018.12.004

Cited by 120 publications

(73 citation statements)

References 23 publications

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“…However, just by stacking many more layers result in Deep Autoencoder. These have been used for very diverse applications like diabetes detection [110], action detection for surveillance [111], feature learning for process pattern recognition [112], denoising for speech enhancement [113], fault diagnosis [114], social image understanding [115], low light image enhancement [116]. Convolutional Neural Network (CNN) is another DL method which although has achieved unprecedented success mainly in image classification (ImageNet, AlexNet, VGG, YOLO, ResNet, DenseNet etc.)…”

Section: B Data-driven Modelingmentioning

confidence: 99%

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

2020

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A digital twin can be defined as an adaptive model of a complex physical system. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

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Section: B Data-driven Modelingmentioning

confidence: 99%

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

2020

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“…The accuracy of the classification is defined as an evaluation measure to compare the results obtained by several methods applied in the dataset. The accuracy of the classification can be given by the equation [31].…”

Section: Resultsmentioning

confidence: 99%

A Smart Glucose Monitoring System for Diabetic Patient

et al. 2020

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Diabetic patients need ongoing surveillance, but this involves high costs for the government and family. The combined use of information and communication technologies (ICTs), artificial intelligence and smart devices can reduce these costs, helping the diabetic patient. This paper presents an intelligent architecture for the surveillance of diabetic disease that will allow physicians to remotely monitor the health of their patients through sensors integrated into smartphones and smart portable devices. The proposed architecture includes an intelligent algorithm developed to intelligently detect whether a parameter has exceeded a threshold, which may or may not involve urgency. To verify the proper functioning of this system, we developed a small portable device capable of measuring the level of glucose in the blood for diabetics and body temperature. We designed a secure mechanism to establish a wireless connection with the smartphone.

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“…Accuracy adaptive fuzzy [34] 89.80 Hybrid SVR [35] 86.13 Rule mining with POA [36] 79.06 Modified PSO [37] 85.19 Re-RX with J48graft [38] 83.83 Re-RX with C4.5 [38] 80.83 QFAM-GA [39] 91.91 DNN-SAE [40] 86.26 SM-RuleMiner [14] 89.87 Proposed Model 96.09…”

Section: Methodsmentioning

confidence: 99%

ENN Ensemble based Neural Network method for Diabetes Classification

Kumari,

2020

IJEAT

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Diabetes is considered as one of the most chronic disease which has serious impact on human health and leading cause of mortality worldwide. The early prediction of diabetes can help clinicians to provide a better diagnosis to the patients. Recently, computed aided diagnosis systems have gained attention due to significant growth in data mining, and machine learning. Several approaches are present based on the machine learning techniques but due to poor classification performance and computational complexity, it becomes difficult to utilize for real-time applications. Ensemble classification approaches have reported a noteworthy improvement in diabetes classification but desired accuracy is still a challenging task. Hence, in this work we introduce a combined hybrid approach called as ENN-Ensemble based neural network approach for diabetes classification. In this approach, a feature selection process is presented using neighboring search technique; the selected features are processed through the feature ranking model to generate the efficient feature subset for better classification accuracy. Finally, these features are learned and classified using neural network classifier. The experimental study shows that the proposed approach achieves better accuracy when compared with the existing techniques.

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Type 2 diabetes data classification using stacked autoencoders in deep neural networks

Cited by 120 publications

References 23 publications

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

A Smart Glucose Monitoring System for Diabetic Patient

ENN Ensemble based Neural Network method for Diabetes Classification

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