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>

scholarly journals Thickness of Intraretinal Layers in Patients with Type 2 Diabetes Mellitus Depending on a Concomitant Diabetic Neuropathy: Results of a Cross-Sectional Study Using Deviation Maps for OCT Data Analysis

Biomedicines ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 190
Author(s):  
Ruby Kala Prakasam ◽  
Aleksandra Matuszewska-Iwanicka ◽  
Dagmar-Christiane Fischer ◽  
Heidrun Schumann ◽  
Diethelm Tschöpe ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
Retinal Thickness ◽  
Early Stage ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
Retinal Layers

Optical coherence tomography (OCT) supports the detection of thickness changes in intraretinal layers at an early stage of diabetes mellitus. However, the analysis of OCT data in cross-sectional studies is complex and time-consuming. We introduce an enhanced deviation map-based analysis (MA) and demonstrate its effectiveness in detecting early changes in intraretinal layer thickness in adults with type 2 diabetes mellitus (T2DM) compared to common early treatment diabetic retinopathy study (ETDRS) grid-based analysis (GA). To this end, we obtained OCT scans of unilateral eyes from 33 T2DM patients without diabetic retinopathy and 40 healthy controls. The patients were categorized according to concomitant diabetic peripheral neuropathy (DN). The results of MA and GA demonstrated statistically significant differences in retinal thickness between patients and controls. Thinning was most pronounced in total retinal thickness and the thickness of the inner retinal layers in areas of the inner macular ring, selectively extending into areas of the outer macular ring and foveal center. Patients with clinically proven DN showed the strongest thinning of the inner retinal layers. MA showed additional areas of thinning whereas GA tended to underestimate thickness changes, especially in areas with localized thinning. We conclude that MA enables a precise analysis of retinal thickness data and contributes to the understanding of localized changes in intraretinal layers in adults with T2DM.

scholarly journals Simvastatin Reverses Podocyte Injury but not Mesangial Expansion in Early Stage Type 2 Diabetes Mellitus

Renal Failure ◽  
2009 ◽  
Vol 31 (6) ◽  
pp. 503-513 ◽  
Author(s):  
P. Wei ◽  
P. R. Grimm ◽  
D. C. Settles ◽  
C. R. Balwanz ◽  
B. J. Padanilam ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Early Stage ◽  
Mesangial Expansion ◽  
Podocyte Injury

Type 2 Diabetes Mellitus with Early Dry Skin Disorder: A Comparison Study between Primary and Tertiary Care in Indonesia

2021 ◽  
Vol 17 ◽  
Author(s):  
Lili Legiawati ◽  
Kusmarinah Bramono ◽  
Wresti Indriatmi ◽  
Em Yunir ◽  
Aditya Indra Pratama
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Primary Care ◽  
Type 2 Diabetes Mellitus ◽  
Early Stage ◽  
Tertiary Care ◽  
Laboratory Data ◽  
Dry Skin ◽  
Cross Sectional

Background: The prevalence of type 2 diabetes mellitus (T2DM) in Indonesia has continued to increase over the years. Management of T2DM is challenging across clinical settings, including primary and tertiary care. Uncontrolled T2DM puts patients at risk of the development of T2DM complications, especially early-stage dry skin that is neglected by most of the patilents. This study aimed to investigate the comparison between the T2DM management profile and dry skin clinical profile of T2DM patients in primary care and tertiary care settings. Methods: The study was conducted as a cross-sectional epidemiological study by comparing T2DM patient profiles in primary and tertiary care. The data collected included sociodemographic, clinical, and laboratory data that were correlated with T2DM and early dry skin related-T2DM. This study included early dry skin within the SRRC score of 3-11 and excluded infection, ulcer, and severe erythema. Results: The patients in primary and tertiary care presented poorly controlled T2DM with median HbA1c levels of 7.8% and 7.6%. The patients in primary care also presented with high triglyceride, 179 mg/dl. Furthermore, several significant differences were found in the duration of T2DM, duration of dry skin, and DM treatment (OAD and insulin). Conclusions: Significant differences in the duration of T2DM, duration of dry skin, and DM treatment (OAD and insulin) might be affected by the parameter of T2DM glycemic control (Blood pressure (BP), body mass index (BMI), HbA1c, random blood glucose (RBG), and triglyceride).

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