High Blood Sugar Levels Research Articles

Comparative study of autonomic function in diabetics and yoga practitioners using Ewing’s battery

ABSTRACT Background: Diabetes mellitus (DM) is a chronic metabolic disorder characterized by high blood sugar levels. It significantly increases the risk of cardiovascular diseases and autonomic neuropathy. Yoga has been shown to improve autonomic function and glycemic control in diabetic patients. Objective: To compare the autonomic nervous system (ANS) function between diabetic patients and regular yoga practitioners using Ewing’s battery tests. Methods: This cross-sectional study included 270 participants, divided into two groups: 135 diabetics (Group I) and 135 yoga practitioners (Group II). Participants’ height, weight, and BMI were measured. ANS function was assessed using Ewing’s battery tests, which included heart rate (HR) and blood pressure (BP) responses to various stimuli. Statistical analysis was performed using SPSS, with significance set at P ≤ 0.05. Results: Diabetic participants showed lower HR responses to standing from a supine posture, the Valsalva maneuver, and slow deep breathing, indicating impaired parasympathetic function. They also exhibited higher BP responses to the cold pressor test, sustained handgrip, and standing, reflecting sympathetic overactivity. In contrast, yoga practitioners demonstrated better autonomic function with higher HR responses and lower BP responses in these tests. The findings suggest that yoga promotes parasympathetic dominance and reduces sympathetic overactivity. Conclusion: Regular yoga practice significantly improves autonomic function in individuals, including those with diabetes. These results support the incorporation of yoga as a complementary therapy in diabetes management to enhance cardiovascular health and reduce autonomic dysfunction.

Next-generation diabetes diagnosis and personalized diet-activity management: A hybrid ensemble paradigm.

Diabetes, a chronic metabolic condition characterised by persistently high blood sugar levels, necessitates early detection to mitigate its risks. Inadequate dietary choices can contribute to various health complications, emphasising the importance of personalised nutrition interventions. However, real-time selection of diets tailored to individual nutritional needs is challenging because of the intricate nature of foods and the abundance of dietary sources. Because diabetes is a chronic condition, patients with this illness must choose a healthy diet. Patients with diabetes frequently need to visit their doctor and rely on expensive medications to manage their condition. It is challenging to purchase medication for chronic illnesses on a regular basis in underdeveloped nations. Motivated by this concept, we suggest a hybrid model that, rather than depending solely on medication to evade a visit to the doctor, can first anticipate diabetes and then suggest a diet and exercise regimen. This research proposes an optimized approach by harnessing machine learning classifiers, including Random Forest, Support Vector Machine, and XGBoost, to develop a robust framework for accurate diabetes prediction. The study addresses the difficulties in predicting diabetes precisely from limited labeled data and outliers in diabetes datasets. Furthermore, a thorough food and exercise recommender system is unveiled, offering individualized and health-conscious nutrition recommendations based on user preferences and medical information. Leveraging efficient learning and inference techniques, the study achieves a meager error rate of less than 30% using an extensive dataset comprising over 100 million user-rated foods. This research underscores the significance of integrating machine learning classifiers with personalized nutritional recommendations to enhance diabetes prediction and management. The proposed framework has substantial potential to facilitate early detection, provide tailored dietary guidance, and alleviate the economic burden associated with diabetes-related healthcare expenses.

Analysis of Factors Affecting Quality of Life in Type II Diabetes Mellitus Patients at Anshari Saleh Hospital Banjarmasin

Type II diabetes mellitus is characterized by high blood sugar levels that trigger complications and reduce the quality of life physically, psychologically, socially, and environmentally. Quality of life is used to evaluate the effectiveness of interventions and monitor public health. The purpose of the study was to analyze the most influential factors on the quality of life in patients with type II diabetes mellitus at Anshari Saleh Hospital, Banjarmasin. The research method used Cross-sectional with Purposive Sampling technique of 83 respondents. The independent variables were age, gender, education, income, complications, duration of suffering, family support and the dependent variable of quality of life using bivariate with Chi-Square, while multivariate analysis with multiple logistic regression used the WHOQOL-BREF questionnaire as an instrument of quality of life. The results of the study showed that there was an influence between education level (p = 0.000), income level (p = 0.008), disease complications (p = 0.002), duration of suffering (p = 0.021), family support (p = 0.021) and education level factors (OR = 60,527) which had the most influence on the quality of life in patients with type II diabetes mellitus. Education level affects the quality of life of diabetes sufferers through understanding and managing the disease. Higher education makes it easier to understand health information, improving quality of life. Meanwhile, low education can reduce quality of life due to difficulty understanding information even though broad knowledge can improve the situation. However, lack of awareness to behave healthily still has a negative impact on the quality of life physically, psychologically, socially, and environmentally. It is hoped that nursing services will provide more continuous education by forming peer groups (diabetes classes), and WhatsApp groups to improve health education for type II diabetes mellitus sufferers according to their level of education in increasing their knowledge.

In-Silico Study of Compounds in The Clove Plant (Syzygium aromaticum L.) As Anti-Diabetic Candidate Targeted at The PPARγ Receptor

Diabetes mellitus is a disease caused by multiple etiologies characterized by high blood sugar levels and accompanied by dysfunction in carbohydrate, lipid, and protein metabolism due to insulin insufficiency. Peroxisome proliferator-activated receptor-γ (PPARγ) is nuclear receptor that plays a role in lowering blood sugar levels by increasing insulin sensitivity through carbohydrate and lipid metabolism, also regulating glucose homeostasis. Clove (Syzygium aromaticum L.) has the potential to reduce blood sugar level based on in vivo tests, but the molecular mechanism is still unknown. This study aims to evaluate the bioactive compounds in clove that have potential as antidiabetic drugs using in silico studies through several analyses: Lipinski's rule of five and ADMET prediction, followed by pharmacophore screening and molecular docking against PPARγ. Our results from ten bioactive compounds found in clove (eugenol, gallic acid, quercetin, kaempferol, β-caryophyllene, eugenitin, myricetin, crategolic acid, stigmasterol, and oleanolic acid), kaempferol exhibited the best potential as a PPARγ receptor agonist, with binding affinity and inhibition constant values of -4.68 kcal/mol and 369.30 μM. Still, it is needed to confirm those activities through a series of in vitro tests to prove the potential of cloves as an antidiabetic candidate targeting PPARγ

Applications of Semaglutide for Various Diseases

Semaglutide is a gluten-like peptide 1 (GLP-1) agonist which is responsible for improving blood sugar control and losing weight. It is clinically approved by the FDA for treatment of type 2 diabetes mellitus and is designed in subcutaneous or oral dosage form. Some other diseases share common conditions with obesity and diabetes such as a high blood sugar level and insulin resistance. These factors along with some neuron issues are responsible for neurodegenerative diseases. Obesity and diabetes are two common worldwide health issues. A number of trials have been conducted to estimate the prevalence and drug effects. Scientists also study related diseases such as Non-alcoholic fatty liver disease to verify the usefulness of treatments. Semaglutide is one of the drugs being measured whether has a potential effect on these diseases. Some pre-clinical studies have indicated its therapeutic potential in non-alcoholic steatohepatitis and neurodegenerative disorders. Semaglutide has proved to be effective in treating lots of disorders, especially diabetes and obesity. However, for other diseases, it still remains uncertain even if there are some studies already tested the effectiveness. Much more detailed and considerate studies should be conducted to verify the impact. Semaglutide, the GLP-1 receptor agonist, has shown a positive impact in treating different diseases. Researchers have tested hepatoprotective and neuroprotective activity in animal and human trials to get more reliable data. It turns out that semaglutide improves insulin resistance and reduces blood sugar levels. It also protects dopaminergic neurons and reduces aggregated α-synuclein which improves Parkinson’s disease and Alzheimer’s disease conditions.

HUBUNGAN PERILAKU PENDERITA DIABETES MELLITUS DENGAN STATUS KEBERSIHAN GIGI DAN MULUT

The background of this research is that oral hygiene is an important aspect of healthcare, especially for diabetes mellitus patients who are prone to oral complications. Diabetes mellitus, characterized by high blood sugar levels, can trigger bacterial growth and worsen oral hygiene. The problem is that the behavior of diabetes mellitus patients greatly affects their oral hygiene status, yet many diabetes mellitus patients exhibit inadequate oral care behaviors. This study aims to determine the relationship between the behavior of diabetes mellitus patients and their oral hygiene status at Puskesmas Mutiara Barat, Pidie District. The research methodology employs a cross-sectional design with a sample of 40 diabetes mellitus patients visiting the Mutiara Barat Health Center in Pidie District. Data collection was conducted through interviews using a questionnaire and assessment of oral and dental hygiene status using the OHIS index. The research took place from March 8 to May 2 2024. The results showed that of the 40 respondents, 62.5% had poor behavior and 62.5% had poor oral and dental hygiene status. Bivariate analysis using the Chi-Square test indicated a significant relationship between the behavior of diabetes mellitus patients and their oral and dental hygiene status (p = 0.000). Poor behavior, such as not regularly maintaining oral hygiene, contributes to poor oral conditions in diabetes mellitus patients. The study concludes that there is a significant relationship between the behavior of diabetes mellitus patients and their oral and dental hygiene status. It is recommended that diabetes mellitus patients improve their oral hygiene habits, control blood sugar levels well, and regularly check their teeth. Health workers are expected to provide comprehensive and ongoing education on the importance of maintaining oral and dental hygiene for diabetes mellitus patients.

Recent Trends on Plants and Agricultural Products as Nutritional Source in Treating Diabetes

A metabolic disease that requires insulin and is marked by consistently high blood sugar levels is known as diabetes mellitus. Many healthcare systems throughout the world have long relied on medicinal herbs as a means of addressing diabetes and its complications. Traditional medicine derived from plant extracts has several advantages over contemporary pharmaceuticals, including lower costs, greater clinical efficacy, and fewer side effects. Pri-marily, the condition has been managed by a range of synthetic medications that improve the altered glycemic state in individuals with diabetes. Synthetic medications work well, but along with their benefits, they come with noticeable adverse effects. Due to the lack of knowledge regarding their chemical composition, preparation method, active bio-actives, potential side effects, and the optimal way to administer them, medicinal plants have not been fully utilised as acceptable drugs in the treatment of diabetes, despite their long history of use as primary health care. Because of a lack of sufficient data on the parameters described earlier, most medicinal plants that show promise as anti-diabetic agents do not make it to the clinical trial phase. Medicinal plants that have been studied in humans with diabetes and shown promise as a treatment for the disease, either alone or in conjunction with other plants, are summarised in this review. Pharmacologically active phytomolecules with an antidia-betic action that are derived from medicinal plants were the primary topic of this review article. Its goal was to discuss their importance in diabetes management and therapy. These all-natural substances have the potential to be successful and alternative diabetes treatments, as well as a new method of approaching the disease.

Genetic biomarkers and machine learning techniques for predicting diabetes: systematic review

Diabetes mellitus is a long-term metabolic condition marked by high blood sugar levels due to issues with insulin production, insulin effectiveness, or a combination of both. It stands as one of the fastest-growing diseases worldwide, projected to afflict 693 million adults by 2045. The escalating prevalence of diabetes and associated health complications (kidney disease, retinopathy, and neuropathy) underscore the imperative to devise predictive models for early diagnosis and intervention. These complications contribute to increased mortality rates, blindness, kidney failure, and an overall diminished quality of life in individuals living with diabetes. While clinical risk factors and glycemic control provide valuable insights, they alone cannot reliably predict the onset of vascular complications. Genetic biomarkers and machine learning techniques have emerged as promising tools for predicting diabetes development risk and associated complications. Despite the emergence of numerous smart AI models for diabetes prediction, there is still a need for a thorough review outlining their progress and challenges. To address this gap, this paper offers a systematic review of the literature on AI-based models for diabetes identification, following the PRISMA extension for scoping reviews guidelines. Our review revealed that multimodal diabetes prediction models outperformed unimodal models. Most studies focused on classical machine learning models, with SNPs being the most used data type, followed by gene expression profiles, while lipidomic and metabolomic data were the least utilized. Moreover, some studies focused on identifying genetic determinants of diabetes complications relied on familial linkage analysis, tailored for robust effect loci. However, these approaches had limitations, including susceptibility to false positives in candidate gene studies and underpowered AI models capabilities due to sample size constraints. The landscape shifted dramatically with the proliferation of genomic datasets, fueled by the emergence of biobanks and the amalgamation of global cohorts. This surge has led to a more than twofold increase in genetic discoveries related to both diabetes and its complications using AI. Our focus here is on these genetic breakthroughs, particularly those empowered by AI models. However, we also highlight the existing gaps in research and underscore the need for further advancements to propel genomic discovery to the next level.

Sugary drink consumption and tooth decay relationship in diabetes mellitus patients

Introduction: Diabetes mellitus is a chronic metabolic disorder characterized by high blood sugar levels. It is a global health problem that can increase the risk of tooth decay and requires conservative dental treatment. Several factors impact the severity of tooth decay, sugary drink consumption is one of them. This study aimed to investigate the association between tooth decay in patients with diabetes mellitus who regularly consume sugary drink versus those who do not. These patients visited Conservative Dentistry Section, Universitas Airlangga Dental Hospital Surabaya, Indonesia, during Jan-Mar 2023. Methods: This study involved 37 patients with diabetes mellitus and posterior tooth decay. Demographic data were gathered from personal interview and dental records. Statistical analysis was done using chi-square analysis. Results: sugary drink consumption has a significant difference (p<0.05) among the patients with posterior tooth decay and diabetes mellitus. Conclusion: sugary drink consumption influences the severity of tooth decay in diabetes mellitus patients.

Gliptin combinations: A Review on analytical methods

Diabetes mellitus is a metabolic disorder that causes high blood sugar levels. Several classes of drugs are used for the treatment. Gliptins inhibit the dipeptidyl peptidase 4 enzyme that prolongs the secretion of insulin in the pancreas and also improve the insulin secretion in response to increased blood glucose which may lead to improved glucose tolerance. Gliptins is an important class of drugs used for the treatment of diabetes in which many drugs such as Alogliptin, Linagliptin, Teneligliptin, Saxagliptin and Sitagliptin are incorporated by which the blood glucose can be controlled. In the present study the authors have summarized the analytical methods so far developed with the combination of Gliptins for the treatment of diabetes. Keywords: Diabetes mellitus, Gliptins combinations, Analytical methods

Recognize and Prevent Diabetes Mellitus with CERDIK at Mangunsari Health Center, Salatiga

Diabetes mellitus is a chronic condition in which the body cannot produce or use insulin effectively, causing high blood sugar levels. Non-pharmacological therapy is one option to prevent and treat diabetes mellitus, for example "Diabetes Diet." Diabetes diet can be done by regulating carbohydrate intake, increasing consumption of fruits and vegetables, avoiding foods high in sugar, and maintaining an active lifestyle. The purpose of this community service is to provide health promotion related to diabetes mellitus and diabetes diet so that the community understands how to treat and prevent diabetes. The method of implementing health promotion is carried out using two techniques: 1) Delivery of material, 2) Demonstration related to diabetes diet. The results of community service showed an increase in participant knowledge about diabetes mellitus and how to treat and prevent it. Participants showed high involvement and enthusiasm during the activity. Evaluation of community service was carried out through oral questions and pre-post tests related to the health promotion material provided. Health promotion related to diabetes mellitus is very important for the community so that they can gain positive knowledge to improve their health.

Heart Failure in Diabetes Mellitus: the Pathogenesis and Treatment

People with diabetes mellitus are more likely to develop heart failure, and those with both conditions often experience a worse prognosis compared to heart failure patients without diabetes. This paper will summarize the pathogenesis of diabetic heart failure and propose treatment and prevention options for diabetic patients with heart failure based on the existing literature and data results. Both types of diabetes lead to high blood sugar levels, which, along with diabetes itself, exert different effects on the heart. As time goes on, it causes heart failure. The outcome demonstrates how chronically elevated blood sugar levels can lead to inflammation and accelerate the development of heart failure. Significant roles are also played by oxidative damage, insulin resistance, and other variables. It has been demonstrated that altering one's lifestyle can avoid diabetic heart failure and improve the quality of life for individuals with asymptomatic diastolic dysfunction and heart failure with preserved ejection fraction (HFpEF).

Optimal control of glucose-insulin dynamics via delay differential model with fractional-order

This paper proposes a delay differential model with fractional order for glucose-insulin endocrine, metabolic regulation model, incorporating beta-cell dynamics to regulate and maintain bloodstream insulin concentration. In the model, two time delays are involved, namely δg and δι, which represent delayed insulin secretion and delayed glucose reduction. A moderate hyperglycemia results in beta-cell growth (negative feedback), while a severe hyperglycemia results in beta-cell reduction (positive feedback). When a time delay passes a bifurcation point, Hopf bifurcation occurs. It is evident from biological findings that the model exhibits periodic oscillations. Furthermore, we present an optimal control problem for external insulin infusions to minimize prolonged high blood sugar levels. Numerical simulations have validated the theoretical results.

In silico design, synthesis and characterization of quercetin derivatives as anti-diabetic agents

Diabetes mellitus is a dominant metabolic condition noticeable by determined high blood sugar levels, stanching from either poor insulin production or insulin resistance. Managing diabetes often includes hindering enzymes, such as alpha-amylase and alpha-glucosidase to control blood glucose points after meals. Natural polyphenols, such as quercetin, have proved major enzyme inhibitory activity, leading to developing interest in reviewing quercetin derivatives as prospective antidiabetic agents. This research emphasizes on the design, synthesis, and assessment of quercetin ester derivatives for their antidiabetic activity by means of in silico docking studies. The key area is to investigate the binding affinity of these derivatives to indispensable enzymes involved in carbohydrate metabolism, while also assessing their drug-likeness and toxicity features. Quercetin ester derivatives, EQ1 and EQ3, were produced through esterification reactions. Quercetin was reacted with benzoic acid and gallic acid, in that order, in toluene, using hydrochloric acid as a catalyst under reflux settings. To calculate the binding affinities, in silico docking studies were supported with human pancreatic alpha-amylase (PDB ID: 2QV4) and acid alpha-glucosidase (PDB ID: 5NN8). Drug-likeness was explored according to Lipinski Rule of Five, and toxicity was projected by computational modeling procedures. Docking studies showed that EQ1 and EQ3 had concrete binding affinities for alpha-amylase and alpha-glucosidase, exceeding the standard drug acarbose. EQ1 attained the highest docking score, with essential interactions such as hydrogen bonding and pi-pi stacking. Drug-likeness evaluation revealed no major rule violations, indicating favorable oral bioavailability for both compounds. Furthermore, toxicity predictions confirmed that EQ1 and EQ3 are non-toxic, underscoring their potential safety. The synthesized quercetin derivatives, EQ1 and EQ3, exhibit considerable promise as antidiabetic agents by inhibiting key enzymes. Their strong binding affinities, favorable drug-likeness characteristics, and non-toxic profiles highlight their potential for further biological studies to validate their effectiveness and therapeutic value in diabetes management.. KEYWORDS :Alpha-amylase/glucosidase inhibition, Analytical evaluation, Drug development, In silico studies, Quercetin ester derivatives, Synthesis.

Comparative Study of Human Serum Paraoxonase-1 (PON-1) Activity in Type 2 Diabetes Mellitus: Insights into Oxidative Stress and Antioxidant Defence

Diabetes mellitus is a disease characterized by chronic high blood sugar levels resulting from defects in insulin secretion, insulin action, or both. Prolonged high blood glucose can increase production of reactive oxygen species, leading to elevated oxidative stress. The enzyme paraoxonase-1 (PON1), made in the liver and transported on high-density lipoprotein (HDL), acts as an antioxidant and can counter oxidative stress. This study aimed to compare PON1 activity between healthy controls, obese type 2 diabetics, and non-obese type 2 diabetics. There were 30 healthy controls, 30 obese type 2 diabetics (group I), and 30 non-obese type. Additionally, fasting lipid profiles, fasting plasma glucose, uric acid, albumin, and total bilirubin levels were determined using a clinical chemistry analyzer. The goal was to evaluate differences in PON1 activity as well as metabolic parameters between the three study groups. The results In comparison to the healthy control group, the diabetes group had Significantly higher blood sugar, serum cholesterol, serum triglycerides, serum LDL, and serum uric acid levels, Compared to the healthy control group, PON was Significantly lower in group II patients, Conclusion that Obese type 2 DM patients had significantly lower PON1 and HDL-C levels, which may indicate That the overweight has lessened biochemical functions for these substances. As dyslipidemia, insulin resistance, and high blood pressure are thought to be crucial factors in the cause of metabolic conditions in obese people, the reduced paraoxonase level may increase their likelihood of developing these conditions.

Evaluation of diabetes care services, data quality, and availability of resources in Ethiopia: Difference-in-differences analysis of the NORAD-WHO NCDs’ midterm project evaluation

BackgroundThe Ethiopian government, supported by NORAD, the WHO, and other partners, is decentralizing diabetes care to primary health units via a task-shifting approach. Despite substantial investment, there is still a lack of up-to-date information on diabetes screening, diagnosis, treatment, and medication availability in the country.ObjectiveThis study assessed the effects of the NORAD-WHO intervention on diabetes care services, data quality, and the availability of infrastructure and medical supplies in Ethiopia.MethodsA quasiexperimental study was conducted across 31 NORAD-WHO project facilities and 62 control facilities in six regions of Ethiopia and Addis Ababa. We used descriptive statistics to assess diabetes screening, diagnosis, treatment services, medication availability, and data quality over 54 months from January 2019 to June 2023. Additionally, we performed a difference-in-differences (DID) regression analysis comparing data from two periods: before the intervention (January to December 2019) and after the intervention (July 2022 to June 2023).ResultsThis study revealed a notable increase in diabetes services, with over 82% of facilities offering screening, early diagnosis, and treatment. Written treatment guidelines are present in three quarters of the facilities. The proportion of trained staff increased from 58% in 2019 to 100% in 2023 across all the evaluated facilities. Intervention facilities had significantly more functional glucometers than did control facilities, averaging four (95% CI: 3.4, 4.6) per month in 2023 compared with 2.5 (95% CI: 2.1, 2.9) in 2019. However, hemoglobin A1C testing remains uncommon. Despite improvements in diabetes service data, issues with missing records, overreporting, and timeliness persist, with an average reporting rate of 99.2% and on-time reporting rate of 51.5%. The NORAD-WHO intervention notably increased the average number of fasting blood sugar tests by 17 per month (95% CI: 12.2–21.8, p = 0.014).ConclusionsThis midterm evaluation revealed a significant increase in the availability of fasting blood sugar tests in the intervention facilities. Additionally, the availability of medical equipment, laboratory services, and medications has improved over the years. Intervention facilities, with more trained healthcare professionals and better resources, outperform control facilities in screening, diagnosing, treating, and managing high blood sugar levels. Notably, intervention facilities screened more clients for diabetes and showed that patients receiving follow-up care achieved better glycemic control than did those at control facilities. While there has been progress in diabetes service data availability, addressing issues such as missing data, overreporting, and reporting timeliness is essential for further improving the quality of diabetes services.Clinical trial numberNot applicable.

Antidiabetic Activity Test of Fe(III) Complex Compound with Arginine Ligand in Male Mice (Mus Musculus L.)

Diabetes mellitus is characterized by high blood sugar levels in the body. Treatment for type 1 diabetes mellitus is with insulin injections while treatment for type 2 diabetes mellitus generally uses oral medication. Currently, people are looking for diabetes drugs made from complex compounds using metals. The development of research and utilization of Fe(III) complex compounds is still limited, thus, in this study the synthesis of Fe(III) complex compounds with arginine ligands was carried out. The results of the research obtained a complex compound of Fe(III)-arginine with 96%, in the form of a brown gel with a sample weight of 0.5601 g. Characterization using a UV-Vis spectrophotometer showed absorption at a wavelength of 203 nm which indicated the absorption of the Fe(III)-arginine complex. The results of FTIR analysis showed a typical absorption of Fe–O and Fe–N bonds at a wavelength of 500–600 nm. The results of the calculation of the mice's body weight decreased when induced by alloxan. After 3 and 4 weeks, the mice's body weight returned to stability. The highest decrease in glucose levels was in dose 2, namely 100 μg/kg bw with a decrease in %GL of 66.72%. The results of this study show that the complex compound Fe(III) arginine can reduce blood glucose levels in mice.

Uncontrolled Diabetes Mellitus Type II Prevalence among Young, Middle, and Old-Aged Adults in Perambalur District Population of Tamil Nadu, India.

This study is aimed to approximate the prevalence of uncontrolled diabetes mellitus among Perambalur District population of South TamilNadu. Diabetes mellitus (DM) is a chronic metabolic disorder due to insulin deficiency or resistance which can affect multiple organs of the body such as heart, kidneys and eyes. Uncontrolled diabetes mellitus (DM), refers to having high blood sugar levels (hyperglycemia). Uncontrolled diabetes is at a greater risk of diabetic complications and a significant health concern in Perambalur District of Tamil Nadu, Southern India, India. A study conducted in Dhanalakshmi Srinivasan Institute of Medical Sciences & Hospital, Perambalur district of Tamil Nadu, Southern India, India. The study included 454 uncontrolled diabetes mellitus subjects of which 60.35% uncontrolled diabetes mellitus in female population and 39.65% uncontrolled diabetes mellitus in male population. The study included 274 female subjects was further divided into three age groups, age group I (25 to 44 years) of 23.38% uncontrolled diabetes mellitus population, age group II (45 to 60 years) of 47.46% uncontrolled diabetes mellitus population, and age group III (61 to 85 years) of 29.20% uncontrolled diabetes mellitus population. The 180 uncontrolled diabetes mellitus male subjects was further divided into three age groups: age group I (25 to 44 years) of 32.22% uncontrolled diabetes mellitus population, age group II (45 to 60 years) of 42.22% uncontrolled diabetes mellitus population, and age group III (61 to 85years) of 25.56% uncontrolled diabetes population. This uncontrolled diabetes mellitus was confirmed by estimating glycated hemoglobin (HbA1C) in these diabetes mellitus population. Our study confirmed that there may be a significant burden of diabetes mellitus related both micro vascular and macro vascular complications in this Perambalur district region of Tamil Nadu, Southern India, India.

Diabetes Prediction Based on KNN, XGBoost, SVM and LR model

Abstract. A. Diabetes mellitus is a chronic metabolic disease characterized by high blood sugar levels due to insulin production problems or insulin resistance. Early identification of diabetes is crucial for preventing associated complications and effectively managing the condition. This study explores the application of four machine learning models, i.e., K-Nearest Neighbor (KNN), XGBoost, Support Vector Machine (SVM) and Logistic Regression (LR) in diabetes prediction. the main goal is to assess and contrast these models' efficacy in identifying diabetes risk, thereby helping healthcare professionals make timely diagnostic and treatment decisions. The results show that the logistic regression model with an AUC value of 0.95 performs much better than the other models, demonstrating excellent sensitivity and specificity in diabetes identification. The XGBoost model also demonstrates considerable predictive accuracy with an AUC value of 0.84, highlighting its ability to effectively handle large-scale datasets. Although the SVM and KNN models had slightly lower AUC values of 0.79, they still provided reliable predictive capabilities. These results demonstrate how machine learning may be used to improve diabetes prediction.

HD-MVCNN: High-density ECG signal based diabetic prediction and classification using multi-view convolutional neural network

Diabetes mellitus, also known as diabetes, is a medical condition marked by high blood sugar levels and impacts a large population worldwide. Treating diabetes is not feasible. It can be managed. Hence, it is crucial to promptly identify a diagnosis of diabetes. This study explores the effects of diabetes on the heart, focusing on heart rate variability (HRV) signals, which can offer valuable information about the existence and seriousness of diabetes through the evaluation of diabetes-related heart problems. Extracting crucial data from the irregular and nonlinear HRV signal can be quite challenging. Studying cardiac diagnostics involves a thorough analysis of electrocardiogram (ECG) signals. Traditional electrocardiogram recordings utilize twelve channels, each capturing a complex combination of activities originating from different regions of the heart. Examining ECG signals recorded on the body’s surface may not be an effective method for studying and diagnosing diabetic issues. The study introduces a research proposal utilizing a high-density resolution electrocardiogram (ECG) system with a minimum of 64 channels and multi-view convolutional neural network classification (HD-MVCNN) to address the mentioned challenges. This framework may help identify the hypoglycaemia effects on brain regions, leading to decreased complexity and increased theta and delta power during scalp electrocardiogram procedures. The convolutional architectural model primarily contributes to enhancement and optimization through its Stochastic Gradient Descent (SGD) along with convolutional layers and according to results, the HD-MVCNN demonstrated better stability and accuracy in comparison to traditional classification models. Thus, HD-MVCNN shows promise as a powerful method for classifying features in diabetes clinical data.