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Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network [article]

Akm Ashiquzzaman, Abdul Kawsar Tushar, Md. Rashedul Islam, Jong-Myon Kim
2017 arXiv   pre-print
Deep learning neural network is used where both fully connected layers are fol-lowed by dropout layers.  ...  In this study, a prediction system for the disease of diabetes is pre-sented where the issue of overfitting is minimized by using the dropout method.  ...  The authors acknowledge department of Computer Science and Engineering, University of Asia Pacific for supporting this research in various ways.  ... 
arXiv:1707.08386v1 fatcat:mj72vv53ffelnje3odoqh34pdu

A Novel Extra Tree Ensemble Optimized DL Framework (ETEODL) for Early Detection of Diabetes

Monika Arya, Hanumat Sastry G, Anand Motwani, Sunil Kumar, Atef Zaguia
2022 Frontiers in Public Health  
Recently, the research has concentrated on improving the prediction model's performance by using ensemble-based and Deep Learning (DL) approaches.  ...  This research utilized a dataset from the UCI Machine learning (ML) repository for an Early-stage prediction of diabetes.  ...  Reduction of overfitting in diabetes prediction using deep learning neural network.  ... 
doi:10.3389/fpubh.2021.797877 pmid:35242738 pmcid:PMC8885585 fatcat:q4wlwdksgvg4venbwjawz7ckq4

Deep LSTM Model for Diabetes Prediction with Class Balancing by SMOTE

Suja A. Alex, NZ Jhanjhi, Mamoona Humayun, Ashraf Osman Ibrahim, Anas W. Abulfaraj
2022 Electronics  
This article details investigations of CNN, CNN-LSTM, ConvLSTM, and deep 1D-convolutional neural network (DCNN) techniques and proposed a SMOTE-based deep LSTM method for diabetes prediction.  ...  This strategy handles class imbalance in the diabetes dataset, and its prediction accuracy is measured.  ...  Acknowledgments: Authors acknowledge thanks to the faculty of computing and informatics, University Malaysia Sabah. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/electronics11172737 fatcat:2omls6du3vfltdnhcd6wggufwi

Diabetic Retinopathy detection by retinal image recognizing [article]

Gilberto Luis De Conto Junior
2020 arXiv   pre-print
The application development took place through convolutional neural networks, which do digital image processing analyzing each image pixel.  ...  The use of VGG-16 as a pre-trained model to the application basis was very useful and the final model accuracy was 82%.  ...  REDUCING OVERFITTING IN DEEP NETWORKS BY DECORRELATING REPRESENTATIONS.  ... 
arXiv:2001.05835v1 fatcat:mmlixwgswbfabaqrmko5mxitky

A Comprehensive Study of Machine Learning Methods on Diabetic Retinopathy Classification

Omer Faruk Gurcan, Omer Faruk Beyca, Onur Dogan
2021 International Journal of Computational Intelligence Systems  
This study proposes an automated DR classification system based on preprocessing, feature extraction, and classification steps using deep convolutional neural network (CNN) and machine learning methods  ...  Grid search and calibration are used in the analysis. This study provides researchers with performance comparisons of different machine learning methods.  ...  We thank scikit learn and XGBoost developers (https://scikitlearn. org,  ... 
doi:10.2991/ijcis.d.210316.001 fatcat:blluzopxkfbmjlorshzy4mg4jq

Machine learning of retinal pathology in optical coherence tomography images

Pushkar Aggarwal
2019 Journal of Medical Artificial Intelligence  
Conclusions: Data augmentation techniques can be used in machine learning to appreciably increase the accuracy of a deep convolutional neural network.  ...  Keras, a neural network framework, was used to retrain Visual Geometry Group 16 (VGG16), a deep neural network, using these images.  ...  Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved  ... 
doi:10.21037/jmai.2019.08.01 fatcat:f4st7pwmjndcpelfi3hwqowmme

An Online Chronic Disease Prediction System Based on Incremental Deep Neural Network

Bin Yang, Lingyun Xiang, Xianyi Chen, Wenjing Jia
2021 Computers Materials & Continua  
One of them is how to make the neural network prediction model continuously adapt and learn disease data of different patients, online.  ...  This paper presents a novel chronic disease prediction system based on an incremental deep neural network.  ...  Note that, the accuracy of [35] , which using DNN for prediction, was quite low. This is because the depth neural network they used had excessive hidden layers and lacked of weight reduction means.  ... 
doi:10.32604/cmc.2021.014839 fatcat:ys7wquf3wfhiji5of6f76ocafy

A novel method for prediction of diabetes mellitus using deep convolutional neural network and long short-term memory

Gorli L Aruna Kumari, Poosapati Padmaja, Jaya G Suma
2022 Indonesian Journal of Electrical Engineering and Computer Science  
In this paper deep convolution neural network can be embedded to long short-term memory networks to recognize early prediction of diabetes and to decrease the complications that can be occurred through  ...  In the proposed based work, a deep learning framework deep combination of convolution neural network and long short-term memory is proposed by embedding both to leverage their respective advantages for  ...  [18] also focused on the overfitting issue of deep learning and presented a new deep learning architecture which uses fully connected layer and dropout layer.  ... 
doi:10.11591/ijeecs.v26.i1.pp404-413 fatcat:wjl7ol5uijfipb3j6uywvyychm

Stochastic variational deep kernel learning based diabetic retinopathy severity grading

Marlin Siebert, Nikolay Tesmer, Philipp Rostalski
2022 Current Directions in Biomedical Engineering  
Automatic detection of DR is mostly done using convolutional neural networks (CNNs) on colour retinal images.  ...  This work in contrast uses stochastic variational deep kernel learning (SVDKL) for DR grading, combining a deep CNN with Gaussian processes (GPs) into a single end-to-end trainable model, which promises  ...  Acknowledgment: This research is part of the project "Patientennahe Smartphone-basierte Diagnostik" (PASBADIA) kindly supported by the Joachim Herz Foundation.  ... 
doi:10.1515/cdbme-2022-1104 fatcat:ncwb7p2kqzb7zewdje2xkct7du

Early Prediction Model for Type-2 Diabetes Based on Lifestyle

Kirti Hirnak, Nikita Chaudhari, Akshay Singh, Deepali Patil, M.D. Patil, V.A. Vyawahare
2020 ITM Web of Conferences  
So we proposed a system based on deep learning approaches that will help to solve a serious problem.  ...  The proposed method focuses on extracting the attributes that gives a result in early detection of Diabetes Mellitus in patients.  ...  Akm Ashiquzzaman. [10] utilized the Neural Network and Deep Learning, an expectation framework for the malady of diabetes is introduced where the issue of overfitting is limited by utilizing the dropout  ... 
doi:10.1051/itmconf/20203203053 fatcat:ug6azqud35ethl4jr52w4d3ffm

Deep Learning Model to Detect Diabetes Mellitus Based on DNA Sequence

Noha E. El-Attar, Bossy M. Moustafa, Wael A. Awad
2022 Intelligent Automation and Soft Computing  
In this research, the authors propose a hybrid deep learning model based on Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) to classify the DNA sequence for the insulin gene to predict  ...  Any mutations in the insulin gene sequence would result in diabetes mellitus.  ...  [1] have used an enhanced deep neural network called diabetes type prediction model to determine the type of the disease that the patient is suffering from.  ... 
doi:10.32604/iasc.2022.019970 fatcat:vweu5ycfdzbclcq4wyfjfu3vli

Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis [chapter]

Alexander Rakhlin, Alexey Shvets, Vladimir Iglovikov, Alexandr A. Kalinin
2018 Lecture Notes in Computer Science  
In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification.  ...  Our approach utilizes several deep neural network architectures and gradient boosted trees classifier. For 4-class classification task, we report 87.2% accuracy.  ...  This unsupervised dimensionality reduction step significantly reduces the risk of overfitting on the next stage of supervised learning.  ... 
doi:10.1007/978-3-319-93000-8_83 fatcat:pganpc3jmjbi7kceo7tgantydi

Early Detection of Diabetic Retinopathy using PCA-Firefly based Deep Learning Model

Thippa Reddy Gadekallu, Neelu Khare, Sweta Bhattacharya, Saurabh Singh, Praveen Kumar Reddy Maddikunta, In-Ho Ra, Mamoun Alazab
2020 Electronics  
Further, Firefly algorithm is implemented for dimensionality reduction. This reduced dataset is fed into a Deep Neural Network Model for classification.  ...  Various machine learning and deep learning approaches have been implemented on diabetic retinopathy dataset for classification and prediction of the disease but majority of them have neglected the aspect  ...  Deep neural network is based on the concept of machine learning and artificial neural networks [7] [8] [9] [10] .  ... 
doi:10.3390/electronics9020274 fatcat:yojgzuxd5jaqzivyfkao5uijs4

Diabetic Retinopathy Detection From Fundus Images Using Multi-Tasking Model With EfficientNet B5

Yash Bhawarkar, Kaustubh Bhure, Vinayak Chaudhary, Bhavana Alte, M.D. Patil, V.A. Vyawahare
2022 ITM Web of Conferences  
In Deep convolutional networks, transfer learning can be utilized to solve the problem of insufficient training data. Previous studies on deep neural networks have been promising.  ...  Due to the huge number of diabetic patients and the need for more accurate and automatic diagnosis, the development of deep neural networks has been acknowledged.  ...  Figure 6 : 6 Figure 6: Flowchart of Proposed Model proposed convolutional Neural newtwork deep learning model Diabetic Retinopathy Detection by means of Deep Learning IEEE 2020.Imabalnce Dataset of EyePacs  ... 
doi:10.1051/itmconf/20224403027 fatcat:gvtqujztwbdcxcjzk2mds3pgkm

Diabetes Prediction Algorithm Using Recursive Ridge Regression L2

Anitha Velu, Menakadevi Thangavelu
2022 Computers Materials & Continua  
Many researchers have proposed techniques to predict this disease through data mining and machine learning methods. In prediction, feature selection is a key concept in preprocessing.  ...  At present, the prevalence of diabetes is increasing because the human body cannot metabolize the glucose level. Accurate prediction of diabetes patients is an important research area.  ...  Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.  ... 
doi:10.32604/cmc.2022.020687 fatcat:edvwlfccqjfbrg7umkqkw2d2ca
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