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Diabetes machine learning

Yukai Li, Huling Li, Hua Yao
The focus of this study is the use of machine learning methods that combine feature selection and imbalanced process (SMOTE algorithm) to classify and predict diabetes follow-up control satisfaction data. After the feature selection and unbalanced process, diabetes follow-up data of the New Urban Area of Urumqi, Xinjiang, was used as input variables of support vector machine (SVM), decision tree, and integrated learning model (Adaboost and Bagging) for modeling and prediction. The experimental results show that Adaboost algorithm produces better classification results...
2018: Computational and Mathematical Methods in Medicine
Jean Debédat, Nataliya Sokolovska, Muriel Coupaye, Simona Panunzi, Rima Chakaroun, Laurent Genser, Garance de Turenne, Jean-Luc Bouillot, Christine Poitou, Jean-Michel Oppert, Matthias Blüher, Michael Stumvoll, Geltrude Mingrone, Séverine Ledoux, Jean-Daniel Zucker, Karine Clément, Judith Aron-Wisnewsky
OBJECTIVE: Roux-en-Y gastric bypass (RYGB) induces type 2 diabetes remission (DR) in 60% of patients at 1 year, yet long-term relapse occurs in half of these patients. Scoring methods to predict DR outcomes 1 year after surgery that include only baseline parameters cannot accurately predict 5-year DR (5y-DR). We aimed to develop a new score to better predict 5y-DR. RESEARCH DESIGN AND METHODS: We retrospectively included 175 RYGB patients with type 2 diabetes with 5-year follow-up...
August 6, 2018: Diabetes Care
Yuheng Chen, Wenna Duan, Parshant Sehrawat, Vaibhav Chauhan, Freddy J Alfaro, Anna Gavrieli, Xingye Qiao, Vera Novak, Weiying Dai
BACKGROUND: Type 2 diabetes mellitus (T2DM) is associated with alterations in the blood-brain barrier, neuronal damage, and arterial stiffness, thus affecting cerebral metabolism and perfusion. There is a need to implement machine-learning methodologies to identify a T2DM-related perfusion pattern and possible relationship between the pattern and cognitive performance/disease severity. PURPOSE: To develop a machine-learning pipeline to investigate the method's discriminative value between T2DM patients and normal controls, the T2DM-related network pattern, and association of the pattern with cognitive performance/disease severity...
August 5, 2018: Journal of Magnetic Resonance Imaging: JMRI
Maryna Neborachko, Aleksandr Pkhakadze, Iryna Vlasenko
Industry 4.0 is an updated concept of smart production, which is identified with the fourth industrial revolution and the emergence of cyber-physical systems. Industry 4.0 is the next stage in the digitization of productions and industries, where such technologies and concepts as the Internet of things, big data, predictive analytics, cloud computing, machine learning, machine interaction, artificial intelligence, robotics, 3D printing, augmented reality. As an area of therapy with the best market potential and one of the most expensive global diseases, diabetes attracts the best healthcare players, who use innovative technologies...
July 29, 2018: Diabetes & Metabolic Syndrome
Ursula Schmidt-Erfurth, Amir Sadeghipour, Bianca S Gerendas, Sebastian M Waldstein, Hrvoje Bogunović
Major advances in diagnostic technologies are offering unprecedented insight into the condition of the retina and beyond ocular disease. Digital images providing millions of morphological datasets can fast and non-invasively be analyzed in a comprehensive manner using artificial intelligence (AI). Methods based on machine learning (ML) and particularly deep learning (DL) are able to identify, localize and quantify pathological features in almost every macular and retinal disease. Convolutional neural networks thereby mimic the path of the human brain for object recognition through learning of pathological features from training sets, supervised ML, or even extrapolation from patterns recognized independently, unsupervised ML...
August 1, 2018: Progress in Retinal and Eye Research
Amrita Roy Chowdhury, Tamojit Chatterjee, Sreeparna Banerjee
Classification of abnormalities from medical images using computer-based approaches is of growing interest in medical imaging. Timely detection of abnormalities due to diabetic retinopathy and age-related macular degeneration is required in order to prevent the prognosis of the disease. Computer-aided systems using machine learning are becoming interesting to ophthalmologists and researchers. We present here one such technique, the Random Forest classifier, to aid medical practitioners in accurate diagnosis of the diseases...
August 4, 2018: Medical & Biological Engineering & Computing
Zhaohan Xiong, Tong Liu, Gary Tse, Mengqi Gong, Patrick A Gladding, Bruce H Smaill, Martin K Stiles, Anne M Gillis, Jichao Zhao
Background: Meta-analysis is a widely used tool in which weighted information from multiple similar studies is aggregated to increase statistical power. However, the exponential growth of publications in key areas of medical science has rendered manual identification of relevant studies increasingly time-consuming. The aim of this work was to develop a machine learning technique capable of robust automatic study selection for meta-analysis. We have validated this approach with an up-to-date meta-analysis to investigate the association between diabetes mellitus (DM) and new-onset atrial fibrillation (AF)...
2018: Frontiers in Physiology
Shaik Mohammad Naushad, Tajamul Hussain, Bobbala Indumathi, Khatoon Samreen, Salman A Alrokayan, Vijay Kumar Kutala
In view of high mortality associated with coronary artery disease (CAD), development of an early predicting tool will be beneficial in reducing the burden of the disease. The database comprising demographic, conventional, folate/xenobiotic genetic risk factors of 648 subjects (364 cases of CAD and 284 healthy controls) was used as the basis to develop CAD risk and percentage stenosis prediction models using ensemble machine learning algorithms (EMLA), multifactor dimensionality reduction (MDR) and recursive partitioning (RP)...
July 11, 2018: Molecular Biology Reports
Wen Cao, Nicholas Czarnek, Juan Shan, Lin Li
Diabetic retinopathy (DR) is an eye abnormality caused by long-term diabetes and it is the most common cause of blindness before the age of 50. Microaneurysms (MAs), resulting from leakage from retinal blood vessels, are early indicators of DR. In this paper, we analyzed MA detectability using small 25 by 25 pixel patches extracted from fundus images in the DIAbetic RETinopathy DataBase - Calibration Level 1 (DIARETDB1). Raw pixel intensities of extracted patches served directly as inputs into the following classifiers: random forest (RF), neural network, and support vector machine...
July 2018: IEEE Transactions on Nanobioscience
Michele Sorelli, Antonia Perrella, Leonardo Bocchi
Vascular ageing is known to be accompanied by arterial stiffening and vascular endothelial dysfunction, and represents an independent factor contributing to the development of cardiovascular disease. The microvascular pulse is affected by the biomechanical alterations of the circulatory system, and has been the focus of studies aiming at the development of non-invasive methods able to extract physiologically relevant features. OBJECTIVE: proposing an approach for the assessment of vascular ageing based on a support vector machine (SVM) learning from features of the pulse contour...
March 9, 2018: IEEE Transactions on Bio-medical Engineering
Konstantia Zarkogianni, Maria Athanasiou, Anastasia C Thanopoulou, Konstantina S Nikita
The estimation of long-term diabetes complications risk is essential in the process of medical decision making. Guidelines for the management of Type 2 Diabetes Mellitus (T2DM) advocate calculating the Cardiovascular Disease (CVD) risk to initiate appropriate treatment. The objective of this study is to investigate the use of sophisticated machine learning techniques towards the development of personalized models able to predict the risk of fatal or non-fatal CVD incidence in T2DM patients. The important challenge of handling the unbalanced nature of the available dataset is addressed, by applying novel ensemble strategies...
October 23, 2017: IEEE Journal of Biomedical and Health Informatics
Ganjar Alfian, Muhammad Syafrudin, Muhammad Fazal Ijaz, M Alex Syaekhoni, Norma Latif Fitriyani, Jongtae Rhee
Current technology provides an efficient way of monitoring the personal health of individuals. Bluetooth Low Energy (BLE)-based sensors can be considered as a solution for monitoring personal vital signs data. In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device, real-time data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition. BLEs were used to gather users’ vital signs data such as blood pressure, heart rate, weight, and blood glucose (BG) from sensor nodes to smartphones, while real-time data processing was utilized to manage the large amount of continuously generated sensor data...
July 6, 2018: Sensors
Stefanie Jauk, Diether Kramer, Stefan Schulz, Werner Leodolter
The use of electronic health records for risk prediction models requires a sufficient quality of input data to ensure patient safety. The aim of our study was to evaluate the influence of incorrect administrative diabetes coding on the performance of a risk prediction model for delirium, as diabetes is known to be one of the most relevant variables for delirium prediction. We used four data sets varying in their correctness and completeness of diabetes coding as input for different machine learning algorithms...
2018: Studies in Health Technology and Informatics
Stephen T Vernon, Thomas Hansen, Katharine A Kott, Jean Y Yang, John F O'Sullivan, Gemma A Figtree
Identification of the four standard modifiable cardiovascular risk factors (SMuRFs)-diabetes mellitus, hyperlipidaemia, hypertension, and cigarette smoking-has allowed the development of risk scores. These have been used in conjunction with primary and secondary prevention strategies targeting SMuRFs to reduce the burden of CAD. Recent studies show that up to 25% of ACS patients do not have any SMuRFs. Thus, SMuRFs do not explain the entire burden of CAD. There appears to be variation at the individual level rendering some individuals relatively susceptible or resilient to developing atherosclerosis...
June 29, 2018: Microcirculation: the Official Journal of the Microcirculatory Society, Inc
Rajsavi S Anand, Paul Stey, Sukrit Jain, Dustin R Biron, Harikrishna Bhatt, Kristina Monteiro, Edward Feller, Megan L Ranney, Indra Neil Sarkar, Elizabeth S Chen
Diabetes constitutes a significant health problem that leads to many long term health issues including renal, cardiovascular, and neuropathic complications. Many of these problems can result in increased health care costs, as well risk of ICU stay and mortality. To date, no published study has used predictive modeling to examine the relative influence of diabetes, diabetic health maintenance, and comorbidities on outcomes in ICU patients. Using the MIMIC-III database, machine learning and binomial logistic regression modeling were applied to predict risk of mortality...
2018: AMIA Summits on Translational Science Proceedings
Era Kim, David S Pieczkiewicz, M Regina Castro, Pedro J Caraballo, Gyorgy J Simon
Because deterioration in overall metabolic health underlies multiple complications of Type 2 Diabetes Mellitus, a substantial overlap among risk factors for the complications exists, and this makes the outcomes difficult to distinguish. We hypothesized each risk factor had two roles: describing the extent of deteriorating overall metabolic health and signaling a particular complication the patient is progressing towards. We aimed to examine feasibility of our proposed methodology that separates these two roles, thereby, improving interpretation of predictions and helping prioritize which complication to target first...
2018: AMIA Summits on Translational Science Proceedings
Katerina Stechova, Miroslav Vanis, Martina Tuhackova, Krzysztof Urbaniec, Milan Kvapil
BACKGROUND: To improve insulin pump therapy results, a special test for patients was devised. The model successfully used to achieve a license to operate different machines was followed. METHODS: The test (a practice and a full run, with a time limit) contained 42 questions, each with four optional choices, and could be answered online. Patients could familiarize themselves with the whole question pool first. Patients could repeat a full run attempt if they failed and were offered focused remedial education...
June 8, 2018: Diabetes Technology & Therapeutics
Christoph Nowak, Axel C Carlsson, Carl Johan Östgren, Fredrik H Nyström, Moudud Alam, Tobias Feldreich, Johan Sundström, Juan-Jesus Carrero, Jerzy Leppert, Pär Hedberg, Egil Henriksen, Antonio C Cordeiro, Vilmantas Giedraitis, Lars Lind, Erik Ingelsson, Tove Fall, Johan Ärnlöv
AIMS/HYPOTHESIS: Multiplex proteomics could improve understanding and risk prediction of major adverse cardiovascular events (MACE) in type 2 diabetes. This study assessed 80 cardiovascular and inflammatory proteins for biomarker discovery and prediction of MACE in type 2 diabetes. METHODS: We combined data from six prospective epidemiological studies of 30-77-year-old individuals with type 2 diabetes in whom 80 circulating proteins were measured by proximity extension assay...
August 2018: Diabetologia
Dimitris Panaretos, Efi Koloverou, Alexandros C Dimopoulos, Georgia-Maria Kouli, Malvina Vamvakari, George Tzavelas, Christos Pitsavos, Demosthenes B Panagiotakos
Statistical methods are usually applied in examining diet-disease associations, whereas factor analysis is commonly used for dietary pattern recognition. Recently, machine learning (ML) has been also proposed as an alternative technique in health classification. In this work, the predictive accuracy of statistical v. ML methodologies as regards the association of dietary patterns on CVD risk was tested. During 2001-2002, 3042 men and women (45 (sd 14) years) were enrolled in the ATTICA study. In 2011-2012, the 10-year CVD follow-up was performed among 2020 participants...
August 2018: British Journal of Nutrition
Mainak Biswas, Venkatanareshbabu Kuppili, Tadashi Araki, Damodar Reddy Edla, Elisa Cuadrado Godia, Luca Saba, Harman S Suri, Tomaž Omerzu, John R Laird, Narendra N Khanna, Andrew Nicolaides, Jasjit S Suri
MOTIVATION: The carotid intima-media thickness (cIMT) is an important biomarker for cardiovascular diseases and stroke monitoring. This study presents an intelligence-based, novel, robust, and clinically-strong strategy that uses a combination of deep-learning (DL) and machine-learning (ML) paradigms. METHODOLOGY: A two-stage DL-based system (a class of AtheroEdge™ systems) was proposed for cIMT measurements. Stage I consisted of a convolution layer-based encoder for feature extraction and a fully convolutional network-based decoder for image segmentation...
July 1, 2018: Computers in Biology and Medicine
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