Selection of Informative Genes to Classify Type 2 Diabetes Mellitus using Support Vector Machine

2019 ◽  
Author(s):  
Firda Nurul Auliah ◽  
Armin Lawi ◽  
Sri Astuti Thamrin. ◽  
Edy Budiman
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Support Vector Machine ◽  
Type 2 Diabetes Mellitus ◽  
Support Vector ◽  
Selection Of

scholarly journals Current Management of Type 2 Diabetes Mellitus: What to Pay Attention to in Outpatient Settings

Doctor Ru ◽  
2021 ◽  
Vol 20 (2) ◽  
pp. 40-44
Author(s):  
N.A. Chernikova ◽  
◽  
O.A. Knyshenko ◽  
◽  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Combined Therapy ◽  
Individual Characteristics ◽  
Antihyperglycemic Agents ◽  
Number Of Patients ◽  
Antihyperglycemic Therapy ◽  
Selection Of

Objective of the Review: To discuss the problem of selecting antihyperglycemic drugs; to identify the trends in prescription of various groups of oral antihyperglycemic agents. Key Points. When type 2 diabetes mellitus (DM2) is diagnosed, a number of patients need prompt combined antihyperglycemic therapy because of a marked carbohydrate metabolism disorder. The prescription paradigm of initial therapy has shifted towards antihyperglycemic agents with established nephro- and cardioprotective effects (sodium-glucose linked transporter-2 inhibitors, glucagon-like peptide-1 receptor agonists). Drugs are recommended depending on presence or absence of a comorbid cardiovascular disease (CVD) and cardiovascular risk factors, and safety as regards hypoglycaemic events; therefore, very often selection of a therapeutic regimen can be challenging. Still, the first-line treatment for patients without CVD is metformin; however, a combined therapy is required in the majority of cases. Poor compliance, continued use of monotherapy, despite the need to boost the therapy, patient’s reluctance to take additional drugs can facilitate occurrence and progression of a lot of associated complications. In such cases, combined medications reducing the amount of tablets and improving compliance are useful. The most common combination of antihyperglycemic drugs is metformin and sulfonylureas. Still, care should be taken because of differences in pharmacokinetics and pharmacodynamics of the molecules in the latter group. High selectivity of some sulfonylureas can evidence their milder effect for glucose level reduction. Sulfonylureas are also cost-effective as compared to other antidiabetic medications. Conclusion. A wide choice of drugs allows a medical professional selecting an optimal antihyperglycemic regimen, taking into account individual characteristics of a patient. Prompt combined medications are a treatment of choice for the majority of patients with DM. Selection of antihyperglycemic drugs is affected by the cost as well. The most important thing is that the drugs are well-studied, efficient and safe. Keywords: type 2 diabetes mellitus, combined therapy, sulphonylurea, Glimepiride, metformin.

scholarly journals Clinical Management of Stable Coronary Artery Disease in Patients With Type 2 Diabetes Mellitus: A Scientific Statement From the American Heart Association

Circulation ◽  
2020 ◽  
Vol 141 (19) ◽  
Author(s):  
Suzanne V. Arnold ◽  
Deepak L. Bhatt ◽  
Gregory W. Barsness ◽  
Alexis L. Beatty ◽  
Prakash C. Deedwania ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Coronary Artery Disease ◽  
Type 2 Diabetes Mellitus ◽  
Coronary Artery ◽  
Cardiovascular Outcomes ◽  
Artery Disease ◽  
Selection Of

Although cardiologists have long treated patients with coronary artery disease (CAD) and concomitant type 2 diabetes mellitus (T2DM), T2DM has traditionally been considered just a comorbidity that affected the development and progression of the disease. Over the past decade, a number of factors have shifted that have forced the cardiology community to reconsider the role of T2DM in CAD. First, in addition to being associated with increased cardiovascular risk, T2DM has the potential to affect a number of treatment choices for CAD. In this document, we discuss the role that T2DM has in the selection of testing for CAD, in medical management (both secondary prevention strategies and treatment of stable angina), and in the selection of revascularization strategy. Second, although glycemic control has been recommended as a part of comprehensive risk factor management in patients with CAD, there is mounting evidence that the mechanism by which glucose is managed can have a substantial impact on cardiovascular outcomes. In this document, we discuss the role of glycemic management (both in intensity of control and choice of medications) in cardiovascular outcomes. It is becoming clear that the cardiologist needs both to consider T2DM in cardiovascular treatment decisions and potentially to help guide the selection of glucose-lowering medications. Our statement provides a comprehensive summary of effective, patient-centered management of CAD in patients with T2DM, with emphasis on the emerging evidence. Given the increasing prevalence of T2DM and the accumulating evidence of the need to consider T2DM in treatment decisions, this knowledge will become ever more important to optimize our patients’ cardiovascular outcomes.

scholarly journals Assessment of Risk of Type 2 Diabetes Mellitus with Stress as a Risk Factor using Classification Algorithms

2019 ◽  
Vol 8 (4) ◽  
pp. 11273-11277
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Logistic Regression ◽  
Risk Factor ◽  
Random Forest ◽  
Health Concern ◽  
Support Vector

Rising prevalence of type 2 diabetes mellitus is a vital health concern today, not only in India but across the world. Several factors including dietary habits, genetics, lack of physical exercise and stress are known to affect the risk of type 2 diabetes. Although awareness has increased to some extent, many people with diabetes have limited knowledge about the risk factors before the diagnosis of disease. For chronic disease prevention there is a necessity to find out such risk factors and manage them appropriately. Statistical techniques can be employed to understand the risk of type 2 diabetes in different age group of people. The objective of the research was to evaluate relationship among stress and type 2 diabetes in people with different age groups by a statistical tool. The proposed method uses three machine learning classifiers namely Support Vector Machine (SVM), Logistic Regression and Random Forest (RF) to detect type 2 diabetes at an early stage. To develop an adaptive model the preprocessing step has been applied. The accuracy of predicting diabetes using SVM, Random Forest and Logistic Regression was 80.17%, 79.37%, 78.67% respectively. The results suggest that as compared to Random Forest and Logistic Regression, SVM is better in predicting occurrence and progress of type 2 diabetes mellitus with stress as a risk factor.

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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