An Optimization-Based Diabetes Prediction Model Using CNN and Bi-Directional LSTM in Real-Time Environment

Madan, Parul; Singh, Vijay Kumar; Chaudhari, Vaibhav; Albagory, Yasser; Dumka, Ankur; Singh, Rajesh; Gehlot, Anita; Rashid, Mamoon; Alshamrani, Sultan S.; AlGhamdi, Ahmed Saeed

doi:10.3390/app12083989

Cited by 69 publications

(20 citation statements)

References 53 publications

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“…There are only a few studies available that diagnose the real-time blood glucose level through spectrogram images. Electronic healthcare records (EHR) data deal with tabular datasets that include PIMA [ 39 ] and Luzhou [ 40 ] datasets.…”

Section: Resultsmentioning

confidence: 99%

AAL and Internet of Medical Things for Monitoring Type-2 Diabetic Patients

et al. 2022

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Due to an aging population, assisted-care options are required so that senior citizens may maintain their independence at home for a longer time and rely less on caretakers. Ambient Assisted Living (AAL) encourages the creation of solutions that can help to optimize the environment for senior citizens with assistance while greatly reducing their challenges. A framework based on the Internet of Medical Things (IoMT) is used in the current study for the implementation of AAL technology to help patients with Type-2 diabetes. A glucose oxide sensor is used to monitor diabetic elderly people continuously. Spectrogram images are created from the recorded data from the sensor to assess and detect aberrant glucose levels. DenseNet-169 examines and analyzes the spectrogram pictures, and messages are sent to caregivers when aberrant glucose levels are detected. The current work describes both the spectrogram image analysis and the signal-to-spectrogram generating method. The study presents a future perspective model for a mobile application for real-time patient monitoring. Benchmark metrics evaluate the application’s performances, including sensitivity, specificity, accuracy, and F1-score. Several cross--validations are used to evaluate the model’s performance. The findings demonstrate that the proposed model can correctly identify patients with abnormal blood glucose levels.

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Section: Resultsmentioning

confidence: 99%

AAL and Internet of Medical Things for Monitoring Type-2 Diabetic Patients

et al. 2022

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“…However, convolutional neural network-based framework cannot extract the temporal information. Madan et al [34] proposed the hybrid model named Convolutional Neural Network-Bidirectional Long Short-term Memory (CNN-BiLSTM) for diabetes risk assessment. However, CNN-BiLSTM failed to capture the correlations between the risk factor categories.…”

Section: R Elated W O Rksmentioning

confidence: 99%

“…6) Convolutional Neural Network-Bidirectional Long Shortterm Memory : Madan et al proposed the CNN-BiLSTM for diabetes risk assessment task [34]. The CNN module contains two parts, each of which consists of a convolutional layer and a max pooling layer.…”

Section: Dmnet: Classificationmentioning

confidence: 99%

“…To compare with our proposed DMNet, we adopt the naive bayes (NB), random forest (RF), gradient boosting decision tree (GBDT), neural network(NN), and state-of-the-art models DCNN [33], CNN-BiLSTM [34] as baseline methods. Since the baseline methods were not used for temporal diabetes data, we concatenate the temporal information in each feature to form the single feature vector.…”

Section: Accuracy = T P + T N + F P + F N (14)mentioning

confidence: 99%

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DMNet: A Personalized Risk Assessment Framework for Elderly People With Type 2 Diabetes

Luo

Tse

et al. 2023

IEEE J. Biomed. Health Inform.

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Type 2 diabetes is the most common chronic disease for the elderly people. This disease is difficult to be cured and causes continued medical expenses. The early and personalized risk assessment of type 2 diabetes is necessary. S o far, various type 2 diabetes risk prediction methods have been proposed. However, these methods have three major issues: 1) not fully considering the importance of personal information and rating information of healthcare system, 2) not adopting the long-term temporal information, and 3) not comprehensively capturing the correlation between the diabetes risk factor categories. To address these issues, the personalized risk assessment framework for elderly people with type 2 diabetes is needed. However, it is very challenging due to two reasons, namely imbalanced label distribution and high-dimensional features. In this paper, we propose diabetes mellitus network framework (DMNet) for type 2 diabetes risk assessment of elderly people. Specifically, we propose tandem long shortterm memory to extract the long-term temporal information of different diabetes risk categories. In addition, the tandem mechanism is used to capture the correlation between the diabetes risk factor categories. To balance the label distribution, we adopt the method of synthetic minority over-sampling technique with Tomek links. To form the better feature representations, we utilize entity embedding to solve the problem of high-dimensional features. To evaluate the performance of our proposed method, we conduct the experiments on a real-world dataset called Research on Early Life and Aging Trends and Effects. The experiment results show that DMNet outperforms the baseline methods in terms of six evaluation metrics (i.e., accuracy of 0.94, balanced accuracy of 0.94, precision of 0.95, F1-score of 0.95, recall of 0.95 and AUC of 0.94).

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“…In this research work, the author used wellknown MLA approaches to examine actual diagnostic medical data based on various risk factors to assess their effectiveness for diabetic probability. Seven MLA were used in this study such as RF, KNN, MLP, SVC, GBC, DT, and LR [24][25][26][27][28][29][30][31][32]. Various statistical criteria were used to compare the analytical results.…”

Section: Literature Reviewmentioning

confidence: 99%

Prevalence and Early Prediction of Diabetes Using Machine Learning in North Kashmir: A Case Study of District Bandipora

Bhat

Selvam

Ansari

et al. 2022

Computational Intelligence and Neuroscience

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Diabetes is one of the biggest health problems that affect millions of people across the world. Uncontrolled diabetes can increase the risk of heart attack, cancer, kidney damage, blindness, and other illnesses. Researchers are motivated to create a Machine Learning methodology that can predict diabetes in the future. Exploiting Machine Learning Algorithms (MLA) is essential if healthcare professionals are able to identify diseases more effectively. In order to improve the medical diagnosis of diabetes this research explored and contrasts various MLA that can identify diabetes risk early. The research includes the analysis on real datasets such as a clinical dataset gathered from a doctor in the Indian district of Bandipora in the years April 2021–Feb2022. MLA are currently important in the healthcare sector due to their prediction abilities. Researchers are using MLA to improve disease prediction and reduce cost. In this Paper author developed a methodology using Machine Learning Algorithms for Diabetes Disease Risk Prediction in North Kashmir. Six MLA have been successfully used in the experimental study such as Random Forest (RF), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Gradient Boost (GB), Decision Tree (DT), and Logistic Regression (LR). RF is the most accurate classifier with the uppermost accuracy rate of 98 percent followed by MLP (90.99%), SVM (92%), GBC (97%), DT (96%), and LR (69%), respectively, with the balanced data set. Lastly, this study enables us to effectively identify the prevalence and prediction of diabetes.

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An Optimization-Based Diabetes Prediction Model Using CNN and Bi-Directional LSTM in Real-Time Environment

Cited by 69 publications

References 53 publications

AAL and Internet of Medical Things for Monitoring Type-2 Diabetic Patients

AAL and Internet of Medical Things for Monitoring Type-2 Diabetic Patients

DMNet: A Personalized Risk Assessment Framework for Elderly People With Type 2 Diabetes

Prevalence and Early Prediction of Diabetes Using Machine Learning in North Kashmir: A Case Study of District Bandipora

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