Prevalence of risk factors promoting Diabetic neuropathy .

Diabetic neuropathy is the worst consequence of diabetes mellitus leading to nerve dysfunction that is the cause of several complications such as pain, loss of sensitivity, damage to body systems, foot ulcers, morbidity and amputations etc. The aim of the present work was to study the prevalence of risk factors and occurrence of diabetic neuropathy in patients with diabetes, and how much diabetic neuropathy complications affect the life of diabetic patients

Diabetes Prediction Using Artificial Neural Network

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks to predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3

Diabetes Classification using Fuzzy Logic and Adaptive Cuckoo Search Optimization Techniques

Diabetic patients can be detected now a days globally. It�s main reason of growth is the incapability of body to produce enough insulin. So, majority of people today are either diabetic or pre-diabetic. Therefore, it is very much required to develop a system that can detect and classify the diabetes in optimal time period effectively and efficiently. So, proposed system make use of fuzzy logic and adaptive cuckoo search optimization algorithm (ACS) for diabetes classification. This work has been carried out in various steps. Firstly, the training dataset�s dimensionality reduction and optimal fuzzy rule generation via ACS optimization technique. Next is fuzzy model design and testing of fuzzified testing dataset. In this paper, outcome of FF-BAT algorithm has been compared with ACS algorithm. Experimental results were examined and it is noticed that ACS algorithm seems to perform better than FF-BAT algorithm

Automatic Screening and Classification of Diabetic Retinopathy Eye Fundus Image

Diabetic Retinopathy (DR) is a disorder of the retinal vasculature. It develops to some degree in nearly all patients with long-standing diabetes mellitus and can result in blindness. Screening of DR is essential for both early detection and early treatment. This thesis aims to investigate automatic methods for diabetic retinopathy detection and subsequently develop an effective system for the detection and screening of diabetic retinopathy. The presented diabetic retinopathy research involves three development stages. Firstly, the thesis presents the development of a preliminary classification and screening system for diabetic retinopathy using eye fundus images. The research will then focus on the detection of the earliest signs of diabetic retinopathy, which are the microaneurysms. The detection of microaneurysms at an early stage is vital and is the first step in preventing diabetic retinopathy. Finally, the thesis will present decision support systems for the detection of diabetic retinopathy and maculopathy in eye fundus images. The detection of maculopathy, which are yellow lesions near the macula, is essential as it will eventually cause the loss of vision if the affected macula is not treated in time. An accurate retinal screening, therefore, is required to assist the retinal screeners to classify the retinal images effectively. Highly efficient and accurate image processing techniques must thus be used in order to produce an effective screening of diabetic retinopathy. In addition to the proposed diabetic retinopathy detection systems, this thesis will present a new dataset, and will highlight the dataset collection, the expert diagnosis process and the advantages of the new dataset, compared to other public eye fundus images datasets available. The new dataset will be useful to researchers and practitioners working in the retinal imaging area and would widely encourage comparative studies in the field of diabetic retinopathy research. It is envisaged that the proposed decision support system for clinical screening would greatly contribute to and assist the management and the detection of diabetic retinopathy. It is also hoped that the developed automatic detection techniques will assist clinicians to diagnose diabetic retinopathy at an early stage

Computing resources sensitive parallelization of neural neworks for large scale diabetes data modelling, diagnosis and prediction

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Diabetes has become one of the most severe deceases due to an increasing number of diabetes patients globally. A large amount of digital data on diabetes has been collected through various channels. How to utilize these data sets to help doctors to make a decision on diagnosis, treatment and prediction of diabetic patients poses many challenges to the research community. The thesis investigates mathematical models with a focus on neural networks for large scale diabetes data modelling and analysis by utilizing modern computing technologies such as grid computing and cloud computing. These computing technologies provide users with an inexpensive way to have access to extensive computing resources over the Internet for solving data and computationally intensive problems. This thesis evaluates the performance of seven representative machine learning techniques in classification of diabetes data and the results show that neural network produces the best accuracy in classification but incurs high overhead in data training. As a result, the thesis develops MRNN, a parallel neural network model based on the MapReduce programming model which has become an enabling technology in support of data intensive applications in the clouds. By partitioning the diabetic data set into a number of equally sized data blocks, the workload in training is distributed among a number of computing nodes for speedup in data training. MRNN is first evaluated in small scale experimental environments using 12 mappers and subsequently is evaluated in large scale simulated environments using up to 1000 mappers. Both the experimental and simulations results have shown the effectiveness of MRNN in classification, and its high scalability in data training. MapReduce does not have a sophisticated job scheduling scheme for heterogonous computing environments in which the computing nodes may have varied computing capabilities. For this purpose, this thesis develops a load balancing scheme based on genetic algorithms with an aim to balance the training workload among heterogeneous computing nodes. The nodes with more computing capacities will receive more MapReduce jobs for execution. Divisible load theory is employed to guide the evolutionary process of the genetic algorithm with an aim to achieve fast convergence. The proposed load balancing scheme is evaluated in large scale simulated MapReduce environments with varied levels of heterogeneity using different sizes of data sets. All the results show that the genetic algorithm based load balancing scheme significantly reduce the makespan in job execution in comparison with the time consumed without load balancing.This work is funded by the EPSRC and China Market Association

Machine Learning and Deep Learning Models for Predicting the Onset of Diabetes: A Pilot Study

Diabetes currently one of the most significant worldwide concerns, and its prevalence is only expected to increase in the future years. In order to monitor glucose levels in the blood and set treatment protocols for diabetes, keeping a regular schedule for checking blood glucose levels is essential. The purpose of widespread adoption of digital health in recent years has been to enhance diabetic healthcare for patients, and as a result, a massive quantity of data has been collected that may be used in the ongoing management of this chronic condition. Deep learning, a relatively new kind of machine learning, is one method that has taken advantage of this trend, and its applications seem promising. In this research, we provide a thorough analysis of how deep learning has been used in the study of diabetes thus far. We conducted a comprehensive literature search and found that this method is most often used in the following settings: diabetes diagnosis, glucose control, and the identification of diabetes-related complications. We have described the most important details regarding the learning models used, the development process, the primary outcomes, and the baseline techniques for performance assessment from the 40 original research publications that we selected based on the search. In the reviewed literature, it becomes clear that several deep learning algorithms and frameworks have outperformed traditional machine learning methods to attain state-of-the-art performance on numerous problems involving diabetes. However, we point out several gaps in the existing literature, such as a dearth of readily available data and a lack of clarity in the interpretation of models. In the near future, these obstacles may be surmounted thanks to the fast advancements in deep learning methodologies which will allow for wider application of this technology in therapeutic settings

Prevalence and Correlates of Diabetic Peripheral Neuropathy in a Saudi Arabic Population: A Cross-Sectional Study

The purpose of this cross-sectional study was to investigate the prevalence and correlates of diabetic peripheral neuropathy (DPN) in a Saudi population. The study population consisted of 552 diabetic participants with an average age of 53.4 years. Among this population, 62.7% were male and 94.9% had type 2 diabetes. The average body mass index was 31.1 kg/m2. DPN was diagnosed based on a combination of reduced vibration perception measured by neurothesiometer and/or reduced light touch perception evaluated by the 10-g Semmes-Weinstein monofilament, as well as neurological symptoms. Information on socio-demographic variables, smoking status, duration of diabetes, and medications was obtained through interviews by physicians. Body weight, height, waist circumference, blood pressure and clinical markers were assessed following standard procedures. The prevalence of DPN in this population was 19.9% (95% CI, 16.7%-23.5%). In the multivariable analyses, longer duration of diabetes [odds ratio (OR) for every 5-year increase, 2.49, 95% CI, 1.75-3.53], abdominal obesity (OR, 2.53, 95% CI, 1.41-4.55), and higher levels of fasting blood glucose (OR for every 1 mmol/L increase, 1.05, 95% CI, 0.99-1.12), creatinine (OR for every 10 µmol/L increase, 1.07, 95% CI, 0.99-1.14) and white blood cell count (OR for every 106/L increase, 1.08, 95% CI, 1.01-1.16) were associated with higher odds of DPN, while oral hypoglycemic medication use was associated with a lower odds of DPN (OR, 0.47, 95% CI, 0.26-0.85). In this large Saudi population, several correlates for DPN, in addition to glycemic control and diabetes duration, were identified, including abdominal obesity, creatinine and white blood cell count

Sistem Pakar Berbasis Android untuk Diagnosis Diabetes Melitus dengan Metode Forward Chaining

Diabetes Mellitus is the biggest cause of death disease. This is beause the lack of public knowledge about the symptoms of disease which is caused delayed in handling. This article presents the development of an expert system application used as a Diabetes Mellitus diagnosis tool on Android mobile. The purpose of application is to detect Diabetes Mellitus based on the type of Diabetes Mellitus symptoms and determine the possibility of the disease occurred. The method for developing an expert system is forward chaining. The implementation of forward chaining method is used to gather information then proceed by applying reasoning with the if-then rule as a result of the conclusion of a diagnosis according to symptoms. The stages in developing this expert system application use the Expert System Development Life Cycle (ESDLC). The result of its development is an expert system used for diagnosis of Diabetes Mellitus according to the symptoms experienced. The expert system application is implemented on the Android mobile. This expert system application displays specific results to displaying a diagnosis of Diabetes Mellitus and displays the percentage of the possibility of the disease. Keywords – Android; Diabetes Mellitus; Diagnose; Forward chaining; Expert system.Diabetes Melitus termasuk salah satu penyakit yang menyebabkan kematian terbesar. Hal tersebut disebabkan pengetahuan masyarakat yang kurang mengenai gejala-gejala penyakit yang ditimbulkan sehingga mengalami keterlambatan penanganan. Artikel ini menyajikan pengembangan aplikasi sistem pakar yang digunakan sebagai perangkat diagnosis Diabetes Melitus pada mobile Android. Tujuan aplikasi ini untuk mendeteksi Diabetes Melitus berdasakan gejala yang sedang dialami oleh seseorang sesuai dengan tipe penyakit Diabetes Melitus dan menentukan persentase kemungkinan terjadinya penyakit tersebut. Dalam pengembangkan aplikasi sistem pakar ini menggunakan metode forward chaining. Penerapan metode forward chaining digunakan untuk mengumpulkan informasi kemudian dilanjutkan dengan mengimplementasikan penalaran dengan aturan if-then sebagai hasil kesimpulan diagnosis sesuai dengan gejala. Tahapan dalam pengembangan aplikasi sistem pakar ini menggunakan Expert System Development Life Cycle (ESDLC). Hasil pengembangannya yaitu sistem pakar yang digunakan untuk diagnosis penyakit Diabetes Melitus sesuai gejala yang dialami. Aplikasi sistem pakar tersebut diimplementasikan pada mobile Android. Aplikasi sistem pakar ini menampilkan hasil yang spesifik yaitu selain menampilkan diagnosis Diabetes Melitus juga menampilkan persentase kemungkinan terjadinya penyakit tersebut pada seseorang. Kata Kunci – Android; Diabetes Melitus; Diagnosis; Forward chaining; Sistem pakar

Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening.

Regular eye screening is essential for the early detection and treatment of the diabetic retinopathy. This paper presents a novel automatic screening system for diabetic retinopathy that focuses on the detection of the earliest visible signs of retinopathy, which are microaneurysms. Microaneurysms are small dots on the retina, formed by ballooning out of a weak part of the capillary wall. The detection of the microaneurysms at an early stage is vital, and it is the first step in preventing the diabetic retinopathy. The paper first explores the existing systems and applications related to diabetic retinopathy screening, with a focus on the microaneurysm detection methods. The proposed decision support system consists of an automatic acquisition, screening and classification of diabetic retinopathy colour fundus images, which could assist in the detection and management of the diabetic retinopathy. Several feature extraction methods and the circular Hough transform have been employed in the proposed microaneurysm detection system, alongside the fuzzy histogram equalisation method. The latter method has been applied in the preprocessing stage of the diabetic retinopathy eye fundus images and provided improved results for detecting the microaneurysms

Analysis for warning factors of type 2 diabetes mellitus complications with Markov blanket based on a Bayesian network model

Background and objective Type 2 diabetes mellitus (T2DM) complications seriously affect the quality of life and could not be cured completely. Actions should be taken for prevention and self-management. Analysis of warning factors is beneficial for patients, on which some previous studies focused. They generally used the professional medical test factors or complete factors to predict and prevent, but it was inconvenient and impractical for patients to self-manage. With this in mind, this study built a Bayesian network (BN) model, from the perspective of diabetic patients’ self-management and prevention, to predict six complications of T2DM using the selected warning factors which patients could have access from medical examination. Furthermore, the model was analyzed to explore the relationships between physiological variables and T2DM complications, as well as the complications themselves. The model aims to help patients with T2DM self-manage and prevent themselves from complications. Methods The dataset was collected from a well-known data center called the National Health Clinical Center between 1st January 2009 and 31st December 2009. After preprocess and impute the data, a BN model merging expert knowledge was built with Bootstrap and Tabu search algorithm. Markov Blanket (MB) was used to select the warning factors and predict T2DM complications. Moreover, a Bayesian network without prior information (BN-wopi) model learned using 10-fold cross-validation both in structure and in parameters was added to compare with other classifiers learned using 10-fold cross-validation fairly. The warning factors were selected according the structure learned in each fold and were used to predict. Finally, the performance of two BN models using warning features were compared with Naïve Bayes model, Random Forest model, and C5.0 Decision Tree model, which used all features to predict. Besides, the validation parameters of the proposed model were also compared with those in existing studies using some other variables in clinical data or biomedical data to predict T2DM complications. Results Experimental results indicated that the BN models using warning factors performed statistically better than their counterparts using all other variables in predicting T2DM complications. In addition, the proposed BN model were effective and significant in predicting diabetic nephropathy (DN) (AUC: 0.831), diabetic foot (DF) (AUC: 0.905), diabetic macrovascular complications (DMV) (AUC: 0.753) and diabetic ketoacidosis (DK) (AUC: 0.877) with the selected warning factors compared with other experiments. Conclusions The warning factors of DN, DF, DMV, and DK selected by MB in this research might be able to help predict certain T2DM complications effectively, and the proposed BN model might be used as a general tool for prevention, monitoring, and self-management