scholarly journals Fuzzy based Risk Predictive Model for Cardiovascular Complication of Patient with Type 2 Diabetes Mellitus and Hypertension

2019 ◽  
Vol 13 (1) ◽  
pp. 49-58 ◽  
Author(s):  
Napa Rachata ◽  
Punnarumol Temdee ◽  
Worasak Rueangsirarak ◽  
Chayapol Kamyod
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Scientific Data ◽  
Cardiovascular Complication ◽  
Key Factors ◽  
Lifestyle Risk Factors ◽  
Proposed Model ◽  
Lifestyle Risk

Cardiovascular diseases are chronic diseases that cause serious morbidity and mortality worldwide. Unfortunately, the patients with type 2 diabetes mellitus and hypertension have a high risk of having a cardiovascular complication. For these reasons, patients with type 2 diabetes mellitus and hypertension should be aware of cardiovascular complication along their healthcare journey. To prevent cardiovascular complication from diabetes and hypertension, accurate risk prediction is required for a long term self-management process. Consequently, this paper proposes a fuzzy logic based method for predicting cardiovascular risk particularly for a patient with type 2 diabetes mellitus and hypertension. This paper also proposes a set of factors based on the patient’s lifestyle as the key factors besides clinical factors because of their implicit impact on the quality of life of the patient. The proposed model thus employs 15 predictors for both clinical and lifestyle risk factors. Additionally, the proposed model is constructed based on the scientific data and implicit knowledge of the experts. The experiment with 121 patients shows that the proposed prediction model provides 96.69% accuracy compared to those decided by the experts.

scholarly journals Lifestyle Risk Factors and Cardiovascular Disease in Cubans and Cuban Americans

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Melissa S. Burroughs Peña ◽  
Dhaval Patel ◽  
Delfin Rodríguez Leyva ◽  
Bobby V. Khan ◽  
Laurence Sperling
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Type 2 Diabetes Mellitus ◽  
Cardiovascular Prevention ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk ◽  
Cuban Americans

Cardiovascular disease is the leading cause of mortality in Cuba. Lifestyle risk factors for coronary heart disease (CHD) in Cubans have not been compared to risk factors in Cuban Americans. Articles spanning the last 20 years were reviewed. The data on Cuban Americans are largely based on the Hispanic Health and Nutrition Examination Survey (HHANES), 1982–1984, while more recent data on epidemiological trends in Cuba are available. The prevalence of obesity and type 2 diabetes mellitus remains greater in Cuban Americans than in Cubans. However, dietary preferences, low physical activity, and tobacco use are contributing to the rising rates of obesity, type 2 diabetes mellitus, and CHD in Cuba, putting Cubans at increased cardiovascular risk. Comprehensive national strategies for cardiovascular prevention that address these modifiable lifestyle risk factors are necessary to address the increasing threat to public health in Cuba.

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

scholarly journals The Gut Microbiome and Type 2 Diabetes Mellitus: Discussing A Complex Relationship

Biomedicines ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 8 ◽  
Author(s):  
Angelos K. Sikalidis ◽  
Adeline Maykish
Keyword(s):  
Diabetes Mellitus ◽  
Insulin Resistance ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Gut Microbiome ◽  
Disease Onset ◽  
The United States ◽  
Key Factors ◽  
Fat Diets

Type 2 diabetes mellitus (T2DM) is a disease that affects over 9% of the United States population and is closely linked to obesity. While obesity was once thought to stem from a sedentary lifestyle and diets high in fat, recent evidence supports the idea that there is more complexity pertinent to the issue. The human gut microbiome has recently been the focus in terms of influencing disease onset. Evidence has shown that the microbiome may be more closely related to T2DM than what was originally thought. High fat diets typically result in poor microbiome heath, which then shifts the gut into a state of dysbiosis. Dysbiosis can then lead to metabolic deregulation, including increased insulin resistance and inflammation, two key factors in the development of T2DM. The purpose of this review is to discuss how microbiome relates to T2DM onset, especially considering obesity, insulin resistance, and inflammation.

scholarly journals A modified mayfly-SVM approach for early detection of type 2 diabetes mellitus

2022 ◽  
Vol 12 (1) ◽  
pp. 524
Author(s):  
Ratna Patil ◽  
Sharvari Tamane ◽  
Shitalkumar Adhar Rawandale ◽  
Kanishk Patil
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Prediction Models ◽  
Early Stage ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Test Accuracy ◽  
Proposed Model

<p>Diabetes mellitus is a chronic disease that affects many people in the world badly. Early diagnosis of this disease is of paramount importance as physicians and patients can work towards prevention and mitigation of future complications. Hence, there is a necessity to develop a system that diagnoses type 2 diabetes mellitus (T2DM) at an early stage. Recently, large number of studies have emerged with prediction models to diagnose T2DM. Most importantly, published literature lacks the availability of multi-class studies. Therefore, the primary objective of the study is development of multi-class predictive model by taking advantage of routinely available clinical data in diagnosing T2DM using machine learning algorithms. In this work, modified mayfly-support vector machine is implemented to notice the prediabetic stage accurately. To assess the effectiveness of proposed model, a comparative study was undertaken and was contrasted with T2DM prediction models developed by other researchers from last five years. Proposed model was validated over data collected from local hospitals and the benchmark PIMA dataset available on UCI repository. The study reveals that modified Mayfly-SVM has a considerable edge over metaheuristic optimization algorithms in local as well as global searching capabilities and has attained maximum test accuracy of 94.5% over PIMA.</p>

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