Classification Of Diabetes Research Articles

Deep learning ensemble framework for multiclass diabetic retinopathy classification

Deep learning ensemble framework for multiclass diabetic retinopathy classification

Hybrid waterwheel plant and stochastic fractal search optimization for robust diabetes classification

Hybrid waterwheel plant and stochastic fractal search optimization for robust diabetes classification

Comparison of Logistic Regression, Random Forest and Adaboost Algorithms for Diabetes Mellitus Classification

Diabetes mellitus is a chronic disease that affects the way the body regulates sugar (glucose). High blood sugar levels can lead to health complications including heart problems, eye disorders, nerve damage, kidney and blood vessel disorders. It is important for early detection of diabetes by utilizing data mining technology. Data mining has various classification models that can be used to detect diabetes, including logistic regression, random forest and adaboost. The comparison of the three algorithms aims to find out which algorithm is most appropriate in the classification of diabetes. From the results obtained, the random forest algorithm has the best performance in the classification of diabetes mellitus compared to other algorithms.

A machine learning approach using conditional normalizing flow to address extreme class imbalance problems in personal health records

BackgroundSupervised machine learning models have been widely used to predict and get insight into diseases by classifying patients based on personal health records. However, a class imbalance is an obstacle that disrupts the training of the models. In this study, we aimed to address class imbalance with a conditional normalizing flow model, one of the deep-learning-based semi-supervised models for anomaly detection. It is the first introduction of the normalizing flow algorithm for tabular biomedical data.MethodsWe collected personal health records from South Korean citizens (n = 706), featuring genetic data obtained from direct-to-customer service (microarray chip), medical health check-ups, and lifestyle log data. Based on the health check-up data, six chronic diseases were labeled (obesity, diabetes, hypertriglyceridemia, dyslipidemia, liver dysfunction, and hypertension). After preprocessing, supervised classification models and semi-supervised anomaly detection models, including conditional normalizing flow, were evaluated for the classification of diabetes, which had extreme target imbalance (about 2%), based on AUROC and AUPRC. In addition, we evaluated their performance under the assumption of insufficient collection for patients with other chronic diseases by undersampling disease-affected samples.ResultsWhile LightGBM (the best-performing model among supervised classification models) showed AUPRC 0.16 and AUROC 0.82, conditional normalizing flow achieved AUPRC 0.34 and AUROC 0.83 during fifty evaluations of the classification of diabetes, whose base rate was very low, at 0.02. Moreover, conditional normalizing flow performed better than the supervised model under a few disease-affected data numbers for the other five chronic diseases – obesity, hypertriglyceridemia, dyslipidemia, liver dysfunction, and hypertension. For example, while LightGBM performed AUPRC 0.20 and AUROC 0.75, conditional normalizing flow showed AUPRC 0.30 and AUROC 0.74 when predicting obesity, while undersampling disease-affected samples (positive undersampling) lowered the base rate to 0.02.ConclusionsOur research suggests the utility of conditional normalizing flow, particularly when the available cases are limited, for predicting chronic diseases using personal health records. This approach offers an effective solution to deal with sparse data and extreme class imbalances commonly encountered in the biomedical context.

Integrating SMOTE-Tomek and Fusion Learning with XGBoost Meta-Learner for Robust Diabetes Recognition

This research aims to develop a robust diabetes classification method by integrating the Synthetic Minority Over-sampling Technique (SMOTE)-Tomek technique for data balancing and using a machine learning ensemble led by eXtreme Gradient Boosting (XGB) as a meta-learner. We propose an ensemble model that combines deep learning techniques such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) with XGB classifier as the base learner. The data used included the Pima Indians Diabetes and Iraqi Society Diabetes datasets, which were processed by missing value handling, duplication, normalization, and the application of SMOTE-Tomek to resolve data imbalances. XGB, as a meta-learner, successfully improves the model's predictive ability by reducing bias and variance, resulting in more accurate and robust classification. The proposed ensemble model achieves perfect accuracy, precision, recall, specificity, and F1 score of 100% on all tested datasets. This method shows that combining ensemble learning techniques with a rigorous preprocessing approach can significantly improve diabetes classification performance.

Sex Differences in the Trajectories of Cognitive Decline and Affected Cognitive Domains Among Older Adults With Controlled and Uncontrolled Glycemia.

We aimed to analyze the trajectories of cognitive decline as a function of the presence of type 2 diabetes and glycemic control in analyzes stratified by sex in an 8-year follow-up period. A total of 1752 men and 2232 women aged ≥50 years who participated in the English Longitudinal Study of Ageing (ELSA), conducted from 2004 to 2012, were analyzed. The outcomes of interest were performance on the cognitive domains of memory, executive function, and temporal orientation as well as the global cognition score. Cognitive performance was standardized in z-scores in strata based on schooling and age. The participants were classified as without diabetes, with controlled glycemia, and with uncontrolled glycemia, according to medical diagnosis, glucose-lowering medications use and HbA1c levels. Generalized linear mixed models controlled by sociodemographic, behavioral, and health-related characteristics were used for the trajectory analyses. No differences in z-scores were found for global cognition or cognitive domains based on diabetes classification in men and women at baseline. More than 8 years of follow up, women with uncontrolled glycemia had a greater decline in z-scores for global cognition (-0.037 SD/year [95% CI: -0.073; -0.001]) and executive function (-0.049 SD/year [95% CI: -0.092; -0.007]) compared with those without diabetes. No significant difference in trajectories of global cognition or any cognitive domain was found in men as a function of diabetes classification. Women with uncontrolled glycemia are at greater risk of a decline in global cognition and executive function than those without diabetes.

Diabetic prediction and classification of risk level using ODDTADC method in big data analytics

Diabetic prediction and classification of risk level using ODDTADC method in big data analytics

A blockchain-machine learning ecosystem for IoT-Based remote health monitoring of diabetic patients

Diabetes poses a global health challenge, demanding continuous monitoring and expert care for effective management. Conventional monitoring methods lack real-time insights and secure data-sharing capabilities, necessitating innovative solutions that leverage emerging technologies. Existing centralized monitoring systems often entail risks such as data breaches and single points of failure, emphasizing the necessity for a secure, decentralized approach that integrates the Internet of Things (IoT), blockchain, and machine learning for efficient and secure diabetes management. This paper introduces a decentralized, blockchain-based framework for remote diabetes monitoring, IoT sensors, machine learning models, and decentralized applications (DApps). The proposed framework comprises five layers: the IoT Sensor Layer, which collects real-time health data from patients; the Blockchain Layer, leveraging smart contracts on the Ethereum blockchain for secure data sharing and transactions; the machine learning Layer, analyzing patient data to detect diabetes; and the DApps Layer, facilitating interactions between patients, doctors, and hospitals. For intelligent decision-making regarding diabetes based on data collected from different sensors, nine machine learning algorithms, including logistic regression, K-nearest neighbors (KNN), support vector machine (SVM), Decision Tree, Random Forest, AdaBoost, stochastic gradient boosting (SGD), and Naive Bayes, were trained and tested on the PIMA dataset. Based on the performance evaluation parameters such as accuracy, recall, F1-score, and the area under the curve (AUC), it was found that the AdaBoost model achieved the highest predictive accuracy of 92.64%, followed by the Decision Tree with an accuracy of 92.21% in diabetes classification.

A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification.

In response to the growing number of diabetes cases worldwide, Our study addresses the escalating issue of diabetic eye disease (DED), a significant contributor to vision loss globally, through a pioneering approach. We propose a novel integration of a Genetic Grey Wolf Optimization (G-GWO) algorithm with a Fully Convolutional Encoder-Decoder Network (FCEDN), further enhanced by a Kernel Extreme Learning Machine (KELM) for refined image segmentation and disease classification. This innovative combination leverages the genetic algorithm and grey wolf optimization to boost the FCEDN's efficiency, enabling precise detection of DED stages and differentiation among disease types. Tested across diverse datasets, including IDRiD, DR-HAGIS, and ODIR, our model showcased superior performance, achieving classification accuracies between 98.5% to 98.8%, surpassing existing methods. This advancement sets a new standard in DED detection and offers significant potential for automating fundus image analysis, reducing reliance on manual examination, and improving patient care efficiency. Our findings are crucial to enhancing diagnostic accuracy and patient outcomes in DED management.

MediDRNet: Tackling category imbalance in diabetic retinopathy classification with dual-branch learning and prototypical contrastive learning

Background and Objective:The classification of diabetic retinopathy (DR) aims to utilize the implicit information in images for early diagnosis, to prevent and mitigate the further worsening of the condition. However, existing methods are often limited by the need to operate within large, annotated datasets to show significant advantages. Additionally, the number of samples for different categories within the dataset needs to be evenly distributed, because the characteristic of sample imbalance distribution can lead to an excessive focus on high-frequency disease categories, while neglecting the less common but equally important disease categories. Therefore, there is an urgent need to develop a new classification method that can effectively alleviate the issue of sample distribution imbalance, thereby enhancing the accuracy of diabetic retinopathy classification. Methods:In this work, we propose MediDRNet, a dual-branch network model based on prototypical contrastive learning. This model adopts prototype contrastive learning, creating prototypes for different levels of lesions, ensuring they represent the core features of each lesion level. It classifies by comparing the similarity between data points and their category prototypes. Our dual-branch network structure effectively resolves the issue of category imbalance and improves classification accuracy by emphasizing subtle differences in retinal lesions. Moreover, our approach combines a dual-branch network with specific lesion-level prototypes for core feature representation and incorporates the convolutional block attention module for enhanced lesion feature identification. Results:Our experiments using both the Kaggle and UWF classification datasets have demonstrated that MediDRNet exhibits exceptional performance compared to other advanced models in the industry, especially on the UWF DR classification dataset where it achieved state-of-the-art performance across all metrics. On the Kaggle DR classification dataset, it achieved the highest average classification accuracy (0.6327) and Macro-F1 score (0.6361). Particularly in the classification tasks for minority categories of diabetic retinopathy on the Kaggle dataset (Grades 1, 2, 3, and 4), the model reached high classification accuracies of 58.08%, 55.32%, 69.73%, and 90.21%, respectively. In the ablation study, the MediDRNet model proved to be more effective in feature extraction from diabetic retinal fundus images compared to other feature extraction methods. Conclusions:This study employed prototype contrastive learning and bidirectional branch learning strategies, successfully constructing a grading system for diabetic retinopathy lesions within imbalanced diabetic retinopathy datasets. Through a dual-branch network, the feature learning branch effectively facilitated a smooth transition of features from the grading network to the classification learning branch, accurately identifying minority sample categories. This method not only effectively resolved the issue of sample imbalance but also provided strong support for the precise grading and early diagnosis of diabetic retinopathy in clinical applications, showcasing exceptional performance in handling complex diabetic retinopathy datasets. Moreover, this research significantly improved the efficiency of prevention and management of disease progression in diabetic retinopathy patients within medical practice. We encourage the use and modification of our code, which is publicly accessible on GitHub: https://github.com/ReinforceLove/MediDRNet.

Classification, Symptoms, Treatment and Preventive Strategies of Diabetes. A guide to the basic

Introduction Diabetes is a chronic metabolic disorder characterized by impaired insulin secretion and action. Risk factors for diabetes include age, obesity, low physical activity, hypertension, lipid disorders, and genetic predisposition. Symptoms of diabetes typically include frequent urination, weight loss, excessive thirst, weakness, fatigue, susceptibility to fungal infections and recurrent boils. Diabetes is a prevalent and significant societal concern, often causing anxiety, uncertainty and a sense of danger upon diagnosis. Aim of the study The aim of the study is to provide a comprehensive analysis of diabetes, including its classification, characteristic symptoms, the most frequently occurring diabetes-related complications, and preventive strategies. Through a detailed discussion of these issues, our goal is to increase public awareness of diabetes and provide readers with comprehensive knowledge about this disease. Material and method This article presents the current state of knowledge about diabetes. A literature review was conducted using the PubMed, Google Scholar and Web of Science databases, utilizing keywords such as "diabetes", "diabetes mellitus", "diabetes complications". Results The number of people with diabetes has continued to rise. Scientists predict that in a few decades, this number could reach more than half a billion globally. Many individuals may not realize that they are at risk for this civilization disease, often due to unhealthy lifestyle choices such as smoking, obesity, and lack of physical activity. Early initiation of treatment is crucial in preventing serious complications. Despite living with diabetes for years, some patients still report insufficient knowledge about their condition. Therefore, educating patients about the nature of the disease and its treatment is essential for improving their quality of life.

Predictive modeling of multi-class diabetes mellitus using machine learning and filtering iraqi diabetes data dynamics.

Diabetes is a persistent metabolic disorder linked to elevated levels of blood glucose, commonly referred to as blood sugar. This condition can have detrimental effects on the heart, blood vessels, eyes, kidneys, and nerves as time passes. It is a chronic ailment that arises when the body fails to produce enough insulin or is unable to effectively use the insulin it produces. When diabetes is not properly managed, it often leads to hyperglycemia, a condition characterized by elevated blood sugar levels or impaired glucose tolerance. This can result in significant harm to various body systems, including the nerves and blood vessels. In this paper, we propose a multiclass diabetes mellitus detection and classification approach using an extremely imbalanced Laboratory of Medical City Hospital data dynamics. We also formulate a new dataset that is moderately imbalanced based on the Laboratory of Medical City Hospital data dynamics. To correctly identify the multiclass diabetes mellitus, we employ three machine learning classifiers namely support vector machine, logistic regression, and k-nearest neighbor. We also focus on dimensionality reduction (feature selection-filter, wrapper, and embedded method) to prune the unnecessary features and to scale up the classification performance. To optimize the classification performance of classifiers, we tune the model by hyperparameter optimization with 10-fold grid search cross-validation. In the case of the original extremely imbalanced dataset with 70:30 partition and support vector machine classifier, we achieved maximum accuracy of 0.964, precision of 0.968, recall of 0.964, F1-score of 0.962, Cohen kappa of 0.835, and AUC of 0.99 by using top 4 feature according to filter method. By using the top 9 features according to wrapper-based sequential feature selection, the k-nearest neighbor provides an accuracy of 0.935 and 1.0 for the other performance metrics. For our created moderately imbalanced dataset with an 80:20 partition, the SVM classifier achieves a maximum accuracy of 0.938, and 1.0 for other performance metrics. For the multiclass diabetes mellitus detection and classification, our experiments outperformed conducted research based on the Laboratory of Medical City Hospital data dynamics.

تحليل مقارن لخوارزميات التعلم الآلي لتصنيف مرض السكري باستخدام تحليل مصفوفة الارتباك

يستخدم العاملين في الرعاية الصحية التعلم الآلي أكثر فأكثر في السنوات الأخيرة لتعزيز نتائج المرضى وخفض التكاليف عليهم. بالإضافة إلى ذلك، تم تنفيذ التعلم الآلي في مجالات مختلفة، بما في ذلك تشخيص الأمراض، وتصنيف مخاطر المرضى، واقتراحات العلاج المخصصة، وتطوير الأدوية. يمكن لخوارزميات التعلم الآلي أن تفحص كميات هائلة من البيانات من السجلات الصحية الإلكترونية، والصور الطبية، ومصادر أخرى لتحديد الأنماط والتنبؤات، والتي يمكن أن تدعم المتخصصين في الرعاية الصحية والخبراء في اتخاذ قرارات مستنيرة، وتعزيز رعاية المرضى، وتحديد حالة المريض الصحية. في هذا الصدد، اختار المؤلف مقارنة أداء ثلاث خوارزميات (الانحدار اللوجستي ، Adaboost ، بايزالساذجة) من خلال معدل التصنيف الصحيح للتنبؤ بمرض السكري من أجل ضمان فعالية التشخيص الدقيق. تم الحصول على مجموعة البيانات المطبقة في هذا العمل من مستودع مؤسسي بجامعة فاندربيلت وهي بيانات متاحة للحميع. استنجتت الدراسة أن ثلاث خوارزميات فعالة للغاية في التنبؤ. بشكل أساسي، كان معدل تصنيف الانحدار اللوجستي و Adaboost أعلى من 92٪ ، وحققت خوارزمية بايز الساذجة معدل تصنيف أعلى من 90٪.

A multi-resolution convolutional attention network for efficient diabetic retinopathy classification

Diabetic retinopathy (DR) is a disorder commonly associated with individuals who have diabetes and is caused by excessive glucose levels in the blood, which induce retinal damage. Manual diagnosis of DR by medical experts is time-consuming; hence, a computer-aided diagnosis (CAD) solution for faster identification and improved efficiency is required. Diagnosis of DR is complex due to the prevalence of inter-class differences and small lesions, making it difficult for convolutional neural networks (CNNs) to identify discriminative regions effectively. The most critical challenge is locating complex patterns connected with visual differences and lesion locations. To tackle the aforementioned challenges, this work presents a novel multi-resolution convolutional attention network (MuR-CAN), which improves overall performance by emphasizing discriminative features. MuR-CAN includes a multi-dilation attention block (MDAB), which uses depth-wise convolution layers with various dilation rates to allow the model to prioritize relevant features and capture multi-scale spatial information. This method provides for simultaneously highlighting fine features and subtle visual patterns. Furthermore, the features extracted from the MuR-CAN are utilized to train the support vector machine (SVM) for DR classification. The integration of the SVM classifier with the proposed CNN architecture ensures improved accuracy. The experimental findings demonstrate that the proposed model outperforms the current state-of-the-art methods.

An improved Tasmanian Devil Optimization algorithm based EfficientNet in convolutional neural network for diabetic retinopathy classification

An improved Tasmanian Devil Optimization algorithm based EfficientNet in convolutional neural network for diabetic retinopathy classification

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images.

Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.

A Review on Diabetes Classification Based on Machine Learning Algorithms

Diabetes, a chronic metabolic disorder, is a significant global health concern affecting millions of individuals worldwide. Early and accurate diagnosis of diabetes is crucial for effective management and prevention of complications. Machine learning (ML) techniques have emerged as powerful tools for analyzing diabetes-related data, aiding in the classification and prediction of diabetes types. This review provides a comprehensive overview of recent advancements in diabetes classification using ML algorithms, highlighting their strengths, limitations, and future directions. Various ML algorithms, including but not limited to support vector machines, decision trees, random forests, artificial neural networks, and ensemble methods, are discussed in details. Furthermore, data preprocessing techniques, feature selection methods, and evaluation metrics employed in diabetes classification studies are examined. Additionally, challenges such as data imbalance, interpretability, and generalization across diverse populations are addressed. Finally, potential avenues for future research to enhance the accuracy and applicability of ML-based diabetes classification systems are proposed.

Classification of Diabetes Mellitus Sufferers Eating Patterns Using K-Nearest Neighbors, Naïve Bayes and Decission Tree

The study investigates three classification algorithms, namely K-Nearest Neighbor (K-NN), Naïve Bayes, and Decision Tree, for the classification of Diabetes Mellitus using a dataset from Kaggle. K-NN relies on distance calculations between test and training data, using the Euclidean distance formula. The choice of k, representing the nearest neighbor, significantly influences K-NN's effectiveness. Naïve Bayes, a probabilistic method, predicts class probabilities based on past events, and it employs the Gaussian distribution method for continuous data. Decision Trees, form prediction models with easily implementable rules. Data collection involves obtaining a Diabetes Mellitus dataset with eight attributes. Data preprocessing includes cleaning and normalization to minimize inconsistencies and incomplete data. The classification algorithms are applied using the Rapidminer tool, and the results are compared for accuracy. Naïve Bayes yields 77.34% accuracy, K-NN performance depends on the chosen k value, and Decision Trees generate rules for classification. The study provides insights into the strengths and weaknesses of each algorithm for diabetes classification

Inaccurate diagnosis of diabetes type in youth: prevalence, characteristics, and implications.

Classifying diabetes at diagnosis is crucial for disease management but increasingly difficult due to overlaps in characteristics between the commonly encountered diabetes types. We evaluated the prevalence and characteristics of youth with diabetes type that was unknown at diagnosis or was revised over time. We studied 2073 youth with new-onset diabetes (median age [IQR] = 11.4 [6.2]years; 50% male; 75% White, 21% Black, 4% other race; overall, 37% Hispanic) and compared youth with unknown versus known diabetes type, per pediatric endocrinologist diagnosis. In a longitudinal subcohort of patients with data for ≥ 3years post-diabetes diagnosis (n = 1019), we compared youth with steady versus reclassified diabetes type. In the entire cohort, after adjustment for confounders, diabetes type was unknown in 62 youth (3%), associated with older age, negative IA-2 autoantibody, lower C-peptide, and no diabetic ketoacidosis (all, p < 0.05). In the longitudinal subcohort, diabetes type was reclassified in 35 youth (3.4%); this was not statistically associated with any single characteristic. In sum, among racially/ethnically diverse youth with diabetes, 6.4% had inaccurate diabetes classification at diagnosis. Further research is warranted to improve accurate diagnosis of pediatric diabetes type.

Efficient diabetic retinopathy classification grading using GAN based EM and PCA learning framework

Efficient diabetic retinopathy classification grading using GAN based EM and PCA learning framework