Learning Methods for Type-2 FLS Based on FCM

2010 ◽  
pp. 224-231 ◽  
Author(s):  
Janusz T. Starczewski ◽  
Łukasz Bartczuk ◽  
Piotr Dziwiński ◽  
Antonino Marvuglia
Keyword(s):  
Learning Methods

scholarly journals The comparison of machine learning methods for prediction study of type 2 diabetes mellitus’s drug design

2020 ◽  
Author(s):  
Nadya Asanul Husna ◽  
Alhadi Bustamam ◽  
Arry Yanuar ◽  
Devvi Sarwinda ◽  
Oky Hermansyah
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Drug Design ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yixiang Deng ◽  
Lu Lu ◽  
Laura Aponte ◽  
Angeliki M. Angelidi ◽  
Vera Novak ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Deep Learning ◽  
Blood Glucose ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Generative Models ◽  
Patient Specific ◽  
Learning Methods ◽  
Glucose Levels

AbstractAccurate prediction of blood glucose variations in type 2 diabetes (T2D) will facilitate better glycemic control and decrease the occurrence of hypoglycemic episodes as well as the morbidity and mortality associated with T2D, hence increasing the quality of life of patients. Owing to the complexity of the blood glucose dynamics, it is difficult to design accurate predictive models in every circumstance, i.e., hypo/normo/hyperglycemic events. We developed deep-learning methods to predict patient-specific blood glucose during various time horizons in the immediate future using patient-specific every 30-min long glucose measurements by the continuous glucose monitoring (CGM) to predict future glucose levels in 5 min to 1 h. In general, the major challenges to address are (1) the dataset of each patient is often too small to train a patient-specific deep-learning model, and (2) the dataset is usually highly imbalanced given that hypo- and hyperglycemic episodes are usually much less common than normoglycemia. We tackle these two challenges using transfer learning and data augmentation, respectively. We systematically examined three neural network architectures, different loss functions, four transfer-learning strategies, and four data augmentation techniques, including mixup and generative models. Taken together, utilizing these methodologies we achieved over 95% prediction accuracy and 90% sensitivity for a time period within the clinically useful 1 h prediction horizon that would allow a patient to react and correct either hypoglycemia and/or hyperglycemia. We have also demonstrated that the same network architecture and transfer-learning methods perform well for the type 1 diabetes OhioT1DM public dataset.

scholarly journals Artificial Intelligence Applications in Type 2 Diabetes Mellitus Care: Focus on Machine Learning Methods

2019 ◽  
Vol 25 (4) ◽  
pp. 248 ◽  
Author(s):  
Shahabeddin Abhari ◽  
Sharareh R. Niakan Kalhori ◽  
Mehdi Ebrahimi ◽  
Hajar Hasannejadasl ◽  
Ali Garavand
Keyword(s):  
Artificial Intelligence ◽  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Application of machine learning methodology to assess the performance of DIABETIMSS program for patients with type 2 diabetes in family medicine clinics in Mexico

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Yue You ◽  
Svetlana V. Doubova ◽  
Diana Pinto-Masis ◽  
Ricardo Pérez-Cuevas ◽  
Víctor Hugo Borja-Aburto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Sensitivity Analysis ◽  
Glycemic Control ◽  
Family Medicine ◽  
Patient Data ◽  
Learning Approaches ◽  
Learning Methods ◽  
The Impact

Abstract Background The study aimed to assess the performance of a multidisciplinary-team diabetes care program called DIABETIMSS on glycemic control of type 2 diabetes (T2D) patients, by using available observational patient data and machine-learning-based targeted learning methods. Methods We analyzed electronic health records and laboratory databases from the year 2012 to 2016 of T2D patients from six family medicine clinics (FMCs) delivering the DIABETIMSS program, and five FMCs providing routine care. All FMCs belong to the Mexican Institute of Social Security and are in Mexico City and the State of Mexico. The primary outcome was glycemic control. The study covariates included: patient sex, age, anthropometric data, history of glycemic control, diabetic complications and comorbidity. We measured the effects of DIABETIMSS program through 1) simple unadjusted mean differences; 2) adjusted via standard logistic regression and 3) adjusted via targeted machine learning. We treated the data as a serial cross-sectional study, conducted a standard principal components analysis to explore the distribution of covariates among clinics, and performed regression tree on data transformed to use the prediction model to identify patient sub-groups in whom the program was most successful. To explore the robustness of the machine learning approaches, we conducted a set of simulations and the sensitivity analysis with process-of-care indicators as possible confounders. Results The study included 78,894 T2D patients, from which 37,767patients received care through DIABETIMSS. The impact of DIABETIMSS ranged, among clinics, from 2 to 8% improvement in glycemic control, with an overall (pooled) estimate of 5% improvement. T2D patients with fewer complications have more significant benefit from DIABETIMSS than those with more complications. At the FMC’s delivering the conventional model the predicted impacts were like what was observed empirically in the DIABETIMSS clinics. The sensitivity analysis did not change the overall estimate average across clinics. Conclusions DIABETIMSS program had a small, but significant increase in glycemic control. The use of machine learning methods yields both population-level effects and pinpoints the sub-groups of patients the program benefits the most. These methods exploit the potential of routine observational patient data within complex healthcare systems to inform decision-makers.

scholarly journals Correction to: Eye‑color and Type‑2 diabetes phenotype prediction from genotype data using deep learning methods

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Muhammad Muneeb ◽  
Andreas Henschel
Keyword(s):  
Type 2 Diabetes ◽  
Deep Learning ◽  
Genotype Data ◽  
Learning Methods ◽  
Phenotype Prediction ◽  
Eye Color

An amendment to this paper has been published and can be accessed via the original article.

scholarly journals 425Artificial Intelligence Approaches to Type 2 Diabetes Risk Prediction and Exploration of Predictive Factors

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Tadao Ooka ◽  
Hiroshi Yokomichi ◽  
Zentaro Yamagata
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Logistic Regression ◽  
Blood Glucose ◽  
Diabetes Risk ◽  
Health Check ◽  
Stepwise Logistic Regression ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Background Major barriers exist in incorporating artificial intelligence into epidemiology, particularly in data interpretation. Thus, we examined the application of highly interpretable machine-learning methods— Random Forest (RF) and Sparse Logistic Regression (SLR)— to a large-scale health check-up dataset, examining the advantages of creating prediction models using these. Methods This study involved 392,791 participants who underwent healthcare checkups in Japan from 1999 to 2018. Participants who received diabetes treatment, or had an HbA1c level of 6.5% or higher, were excluded. The objective variable examined was type 2 diabetes onset over five years. Each prediction model was created using 26 health status items over three consecutive years. We examined three analytical methods to compare their predictive powers: RF, SLR, and a multivariate stepwise logistic regression (MSLR) as a conventional method. Variable Importance (VI) was calculated in the RF analysis, with Standard Regression Coefficients (SRC) being calculated in the SLR and MSLR analyses. Results Predictive accuracy is highest in the SLR model (AUC:0.955), followed by the RF model (AUC:0.949), and then the MSLR model (AUC:0.939). The RF model measures blood glucose, HbA1c, height, red blood cells, and aspartate transaminase with a higher predictive power. In the SLR model, HbA1c, blood glucose, systolic blood pressure, HDL-Cholesterol, and age have higher SRC. Conclusions Machine learning techniques enable more accurate diabetes risk predictions than existing methods and suggest new ways of identifying associated predictors. Key messages Applying machine-learning methods to health check-up data achieves a high accuracy in predicting type 2 diabetes while maintaining data interpretability.

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