scholarly journals Application of an artificial neural network model for diagnosing type 2 diabetes mellitus and determining the relative importance of risk factors

2018 ◽  
Vol 40 ◽  
pp. e2018007 ◽  
Author(s):  
Shiva Borzouei ◽  
Ali Reza Soltanian
Keyword(s):  
Neural Network ◽  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Artificial Neural Network ◽  
Type 2 Diabetes Mellitus ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Relative Importance

The performance of artificial neural network, logistic regression, and random forest models to predict breast cancer in patients with type 2 diabetes mellitus (Preprint)

2019 ◽  
Author(s):  
Chia-Hung Kao
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Artificial Neural Network ◽  
Type 2 Diabetes Mellitus ◽  
Logistic Regression ◽  
Breast Cancer Risk ◽  
Cancer Risk

BACKGROUND Breast cancer incidence may be higher among patients with type 2 diabetes mellitus (T2DM) compared with the general population. This study evaluated the performance of three models for predicting breast cancer risk in patients with T2DM. OBJECTIVE This study evaluated the performance of three models for predicting breast cancer risk in patients with T2DM. METHODS In total, 1,267,867 patients with newly diagnosed T2DM between 2000 and 2012 were identified from Taiwan National Health Insurance Research Database. By employing their data, we created prediction models for detecting an increased risk of subsequent breast cancer development in T2DM patients. The available potential risk factors for breast cancer were also collected for adjustment in the analyses. The Synthetic Minority Oversampling Technique (SMOTE) was used to augment data points in the minority class. Each data point was randomly allocated to the training and test sets at a ratio of approximate 39:1. The performance of artificial neural network (ANN), logistic regression (LR), and random forest (RF) models were determined using the recall, precision, F1 score, and area under receiver operating characteristic curve (AUC). RESULTS The AUCs of all three models were significantly higher than the area of 0.5 for the null hypothesis (0.959, 0.865, and 0.834 for RF, ANN, and LR models, respectively). The RF model has the largest AUC among all models; moreover, it had the highest values in all other metrics. CONCLUSIONS Although all three models could accurately predict high breast cancer risk in patients with T2DM in Taiwan, the RF model demonstrated the best performance. CLINICALTRIAL This is not a chinical trial.

ASSESSMENT OF DEMENTIA IN THE TYPE 2 DIABETES BY MOCA

2017 ◽  
pp. 35-44
Author(s):  
Dinh Toan Nguyen
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
High Sensitivity ◽  
Cross Sectional ◽  
Average Value ◽  
Lifestyle Risk ◽  
Diabetes Mellitus Risk

Background: Studies show that diabetes mellitus is the greatest lifestyle risk factor for dementia. Appropriate management and treatment of type 2 diabetes mellitus could prevent the onset and progression of mild cognitive impairment to dementia. MoCA test is high sensitivity with mild dementia but it have not been used and studied widespread in Vietnam. Aim: 1. Using MoCA and MMSE to diagnose dementia in patients with type 2 diabetes mellitus. 2. Assessment of the relationship between dementia and the risk factors. Methods: cross-sectional description in 102 patients with type 2 diabetes mellitus. The Mini-Mental State Examination(MMSE) and the Montreal Cognitive Assessment (MoCA) were used to assess cognitive function. The diagnosis of dementia was made according to Diagnostic and Statistical Manual of Mental Disorders. Results: The average value for MoCA in the group of patients with dementia (15.35 ± 2.69) compared with non-dementia group (20.72 ± 4.53). The sensitivity and specificity of MoCA were 84.8% and 78.3% in identifying individuals with dementia, and MMSE were 78.5% and 82.6%, respectively. Using DSMIV criteria as gold standard we found MoCA and MMSE were more similar for dementia cases (AUC 0.871 and 0.890). The concordance between MoCA and MMSE was moderate (kappa = 0.485). When considering the risk factors, the education,the age, HbA1c, dyslipidemia, Cholesterol total related with dementia in the type 2 diabetes. Conclusion: MoCA scale is a good screening test of dementia in patients with type 2 diabetes mellitus.When compared with the MMSE scale, MoCA scale is more sensitive in detecting dementia. Key words: MoCA, dementia, type 2 diabetes mellitus, risk factors

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