Deep convolutional neural network for diabetes mellitus prediction

Recent trends in diabetes mellitus diagnosis: an in-depth review of artificial intelligence-based techniques

Diabetes mellitus (DM) is a highly prevalent chronic condition with significant health and economic impacts; therefore, an accurate diagnosis is essential for the effective management and prevention of its complications. This review explores the latest advances in artificial intelligence (AI) focusing on machine learning (ML) and deep learning (DL) for the diagnosis of diabetes. Recent developments in AI-driven diagnostic tools were analyzed, with an emphasis on breakthrough methodologies and their real-world clinical applications. This review also discusses the role of various data sources, datasets, and preprocessing techniques in enhancing diagnostic accuracy. Key advancements in integrating AI into clinical workflows and improving early detection are highlighted along with challenges related to model interpretability, ethical considerations, and practical implementation. By offering a comprehensive overview of these advancements and their implications, this review contributes significantly to the understanding of how AI technologies can enhance the diagnosis of diabetes and support their integration into clinical practice, thereby aiming to improve patient outcomes and reduce the burden of diabetes.

Nested genetic algorithm-based classifier selection and placement in multi-level ensemble framework for effective disease diagnosis

Effective disease diagnosis is a critical unmet need on a global scale. The intricacies of the numerous disease mechanisms and underlying symptoms make developing a model for early diagnosis and effective treatment extremely difficult. Machine learning (ML) can help to solve some of these issues. Recently, various ensemble-based ML models have benefited clinicians in early diagnosis. However, one of the most difficult challenges in multi-level ensemble approaches is the classifier selection and their placement in the ensemble framework as it improves the overall performance. Let m classifiers have to select from n classifiers there are ( n m ) ways. Again, these ( n m ) possibilities can be arranged in m ! ways. Finding the best m classifiers and their positions from total ( n m ) m ! ways is a challenging and hard problem. To address this challenge, a dynamic three-level ensemble framework is proposed. A nested Genetic Algorithm (GA) and ensemble-based fitness function are employed to optimize the classifier selection and their placement in a three-level ensemble framework. Our approach used eleven classifiers and chose seven classifiers by maximizing the fitness function. The proposed model experiments on 12 disease datasets. The proposed model outperformed in terms of accuracy, F1, and G-measure on the Chronic Kidney Disease (CKD) dataset is 0.987, 0.988, and 0.989, respectively. In terms of AUC on the Heart disease dataset (HDD) is 0.998 and in terms of recall on the Hypothyroid disease dataset (HyDD) is 0.988. In addition, the proposed model superiority is statically evaluated by Wilcoxon-Signed-Rank (WSR) test compared with other ensemble models, such as random forest (RF), bagging classifier (BC), XGBoost (XGB), and gradient boost classifier (GBC) with probability value p < 0.05 results shows all the traditional ensemble model differs with proposed model and also effective size evaluated with using the matched-pairs rank biserial correlation coefficient wc and statistical results shows effective size is large with RF and BC and effective size is medium with XGB and GBC. Proposed model has outperformed comparing with State-Of-The-Art (SOTA) ensemble and non-ensemble models. Further, the proposed model outperformed in terms of the ROC curve in the majority of the disease datasets. The results suggest the usage of the proposed model for disease diagnosis applications.

Next-generation diabetes diagnosis and personalized diet-activity management: A hybrid ensemble paradigm

Diabetes, a chronic metabolic condition characterised by persistently high blood sugar levels, necessitates early detection to mitigate its risks. Inadequate dietary choices can contribute to various health complications, emphasising the importance of personalised nutrition interventions. However, real-time selection of diets tailored to individual nutritional needs is challenging because of the intricate nature of foods and the abundance of dietary sources. Because diabetes is a chronic condition, patients with this illness must choose a healthy diet. Patients with diabetes frequently need to visit their doctor and rely on expensive medications to manage their condition. It is challenging to purchase medication for chronic illnesses on a regular basis in underdeveloped nations. Motivated by this concept, we suggest a hybrid model that, rather than depending solely on medication to evade a visit to the doctor, can first anticipate diabetes and then suggest a diet and exercise regimen. This research proposes an optimized approach by harnessing machine learning classifiers, including Random Forest, Support Vector Machine, and XGBoost, to develop a robust framework for accurate diabetes prediction. The study addresses the difficulties in predicting diabetes precisely from limited labeled data and outliers in diabetes datasets. Furthermore, a thorough food and exercise recommender system is unveiled, offering individualized and health-conscious nutrition recommendations based on user preferences and medical information. Leveraging efficient learning and inference techniques, the study achieves a meager error rate of less than 30% using an extensive dataset comprising over 100 million user-rated foods. This research underscores the significance of integrating machine learning classifiers with personalized nutritional recommendations to enhance diabetes prediction and management. The proposed framework has substantial potential to facilitate early detection, provide tailored dietary guidance, and alleviate the economic burden associated with diabetes-related healthcare expenses.

A Computer-Aided Screening Solution for the Identification of Diabetic Neuropathy From Standing Balance by Leveraging Multi-Domain Features

The early diagnosis of diabetic neuropathy (DN) is fundamental in order to enact timely therapeutic strategies for limiting disease progression. In this work, we explored the suitability of standing balance task for identifying the presence of DN. Further, we proposed two diagnosis pathways in order to succeed in distinguishing between different stages of the disease. We considered a cohort of non-neuropathic (NN), asymptomatic neuropathic (AN), and symptomatic neuropathic (SN) diabetic patients. From the center of pressure (COP), a series of features belonging to different description domains were extracted. In order to exploit the whole information retrievable from COP, a majority voting ensemble was applied to the output of classifiers trained separately on different COP components. The ensemble of kNN classifiers provided over 86% accuracy for the first diagnosis pathway, made by a 3-class classification task for distinguishing between NN, AN, and SN patients. The second pathway offered higher performances, with over 97% accuracy in identifying patients with symptomatic and asymptomatic neuropathy. Notably, in the last case, no asymptomatic patient went undetected. This work showed that properly leveraging all the information that can be mined from COP trajectory recorded during standing balance is effective for achieving reliable DN identification. This work is a step toward a clinical tool for neuropathy diagnosis, also in the early stages of the disease.

Revolutionizing Early Disease Detection: A High-Accuracy 4D CNN Model for Type 2 Diabetes Screening in Oman

The surge of diabetes poses a significant global health challenge, particularly in Oman and the Middle East. Early detection of diabetes is crucial for proactive intervention and improved patient outcomes. This research leverages the power of machine learning, specifically Convolutional Neural Networks (CNNs), to develop an innovative 4D CNN model dedicated to early diabetes prediction. A region-specific dataset from Oman is utilized to enhance health outcomes for individuals at risk of developing diabetes. The proposed model showcases remarkable accuracy, achieving an average accuracy of 98.49% to 99.17% across various epochs. Additionally, it demonstrates excellent F1 scores, recall, and sensitivity, highlighting its ability to identify true positive cases. The findings contribute to the ongoing effort to combat diabetes and pave the way for future research in using deep learning for early disease detection and proactive healthcare.

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

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. So 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 short-term 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).

A Novel Proposal for Deep Learning-Based Diabetes Prediction: Converting Clinical Data to Image Data

Diabetes, one of the most common diseases worldwide, has become an increasingly global threat to humans in recent years. However, early detection of diabetes greatly inhibits the progression of the disease. This study proposes a new method based on deep learning for the early detection of diabetes. Like many other medical data, the PIMA dataset used in the study contains only numerical values. In this sense, the application of popular convolutional neural network (CNN) models to such data are limited. This study converts numerical data into images based on the feature importance to use the robust representation of CNN models in early diabetes diagnosis. Three different classification strategies are then applied to the resulting diabetes image data. In the first, diabetes images are fed into the ResNet18 and ResNet50 CNN models. In the second, deep features of the ResNet models are fused and classified with support vector machines (SVM). In the last approach, the selected fusion features are classified by SVM. The results demonstrate the robustness of diabetes images in the early diagnosis of diabetes.

Weighted entropy deep features on hybrid RNN with LSTM for glucose level and diabetes prediction

Glucose level regulation with essential advice regarding diabetes must be provided to the patients to maintain their diet for diabetes treatment. Therefore, the academic community has focused on implementing novel glucose prediction techniques for decision support systems. Recent computational techniques for diagnosing diabetes have certain limitations, and also they are not evaluated under various datasets obtained from the different people of various countries. This generates inefficiency in the prediction systems to apply it in real-time applications. This paper plans to suggest a hybrid deep learning model for diabetes prediction and glucose level classification. Two benchmark datasets are used in the data collection process for experimenting. Initially, the deep selected features were extracted by the Convolutional Neural Network (CNN). Further, weighted entropy deep features are extracted, where the tuning of weight is taken place by the Modified Escaping Energy-based Harris Hawks Optimization. These features are processed in the glucose level classification using the modified Fuzzy classifier for classifying the high-level and low-level glucose. Further, glucose prediction is done by the Hybrid Recurrent Neural Network (RNN), and Long Short Term Memory (LSTM) termed R-LSTM with parameter optimization. From the experimental result, In the dataset 2 analyses on SMAPE, the MEE-HHO-R-LSTM is 12.5%, 87.5%, 50%, 12.5%, and 2.5% better than SVM, LSTM, DNN, RNN, and RNN-LSTM, at the learning percentage of 75%. The analytical results enforce that the suggested methods attain enhanced prediction performance concerning the evaluation metrics compared to conventional prediction models.

Analysis and Recognition of Clinical Features of Diabetes Based on Convolutional Neural Network

Diabetes mellitus is a common chronic noncommunicable disease, the main manifestation of which is the long-term high blood sugar level in patients due to metabolic disorders. However, due to excessive reliance on the clinical experience of ophthalmologists, our diagnosis takes a long time, and it is prone to missed diagnosis and misdiagnosis. In recent years, with the development of deep learning, its application in the auxiliary diagnosis of diabetic retinopathy has become possible. How to use the powerful feature extraction ability of deep learning algorithm to realize the mining of massive medical data is of great significance. Therefore, under the action of computer-aided technology, this paper processes and analyzes the retinal images of the fundus through traditional image processing and convolutional neural network-related methods, so as to achieve the role of assisting clinical treatment. Based on the admission records of diabetic patients after data analysis and feature processing, this paper uses an improved convolutional neural network algorithm to establish a model for predicting changes in diabetic conditions. The model can assist doctors to judge the patient's treatment effect by using it based on the case records of inpatient diagnosis and treatment and to predict the risk of readmission of inpatients after discharge. It also can help to judge the effectiveness of the treatment plan. The results of the study show that the model proposed in this paper has a lower probability of misjudging patients with poor recovery as good recovery, and the prediction is more accurate.

Prediction of Diabetes through Retinal Images Using Deep Neural Network

Microvascular problems of diabetes, such as diabetic retinopathy and macular edema, can be seen in the eye's retina, and the retinal images are being used to screen for and diagnose the illness manually. Using deep learning to automate this time-consuming process might be quite beneficial. In this paper, a deep neural network, i.e., convolutional neural network, has been proposed for predicting diabetes through retinal images. Before applying the deep neural network, the dataset is preprocessed and normalised for classification. Deep neural network is constructed by using 7 layers, 5 kernels, and ReLU activation function, and MaxPooling is implemented to combine important features. Finally, the model is implemented to classify whether the retinal image belongs to a diabetic or nondiabetic class. The parameters used for evaluating the model are accuracy, precision, recall, and F1 score. The implemented model has achieved a training accuracy of more than 95%, which is much better than the other states of the art algorithms.