Description: Welcome to the Diabetes Prediction Dataset, a valuable resource for researchers, data scientists, and medical professionals interested in the field of diabetes risk assessment and prediction. This dataset contains a diverse range of health-related attributes, meticulously collected to aid in the development of predictive models for identifying individuals at risk of diabetes. By sharing this dataset, we aim to foster collaboration and innovation within the data science community, leading to improved early diagnosis and personalized treatment strategies for diabetes.

Columns: 1. Id: Unique identifier for each data entry. 2. Pregnancies: Number of times pregnant. 3. Glucose: Plasma glucose concentration over 2 hours in an oral glucose tolerance test. 4. BloodPressure: Diastolic blood pressure (mm Hg). 5. SkinThickness: Triceps skinfold thickness (mm). 6. Insulin: 2-Hour serum insulin (mu U/ml). 7. BMI: Body mass index (weight in kg / height in m^2). 8. DiabetesPedigreeFunction: Diabetes pedigree function, a genetic score of diabetes. 9. Age: Age in years. 10. Outcome: Binary classification indicating the presence (1) or absence (0) of diabetes.

Utilize this dataset to explore the relationships between various health indicators and the likelihood of diabetes. You can apply machine learning techniques to develop predictive models, feature selection strategies, and data visualization to uncover insights that may contribute to more accurate risk assessments. As you embark on your journey with this dataset, remember that your discoveries could have a profound impact on diabetes prevention and management.

Please ensure that you adhere to ethical guidelines and respect the privacy of individuals represented in this dataset. Proper citation and recognition of this dataset's source are appreciated to promote collaboration and knowledge sharing.

Start your exploration of the Diabetes Prediction Dataset today and contribute to the ongoing efforts to combat diabetes through data-driven insights and innovations.

Detailed dataset comprising health and demographic data of 100,000 individuals, aimed at facilitating diabetes-related research and predictive modeling. This dataset includes information on gender, age, location, race, hypertension, heart disease, smoking history, BMI, HbA1c level, blood glucose level, and diabetes status.

Dataset Use Cases This dataset can be used for various analytical and machine learning purposes, such as:

Predictive Modeling: Build models to predict the likelihood of diabetes based on demographic and health-related features. Health Analytics: Analyze the correlation between different health metrics (e.g., BMI, HbA1c level) and diabetes. Demographic Studies: Examine the distribution of diabetes across different demographic groups and locations. Public Health Research: Identify risk factors for diabetes and target interventions to high-risk groups. Clinical Research: Study the relationship between comorbid conditions like hypertension and heart disease with diabetes. Potential Analyses Descriptive Statistics: Summarize the dataset to understand the central tendencies and dispersion of features. Correlation Analysis: Identify the relationships between features. Classification Models: Use machine learning algorithms to classify individuals as diabetic or non-diabetic. Trend Analysis: Analyze trends over the years to see how diabetes prevalence has changed. clinical_notes: clinical summaries based on patient attributes

United States US: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 10.790 % in 2017. United States US: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 10.790 % from Dec 2017 (Median) to 2017, with 1 observations. United States US: Diabetes Prevalence: % of Population Aged 20-79 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes.; ; International Diabetes Federation, Diabetes Atlas.; Weighted average;

These datasets provide de-identified insurance data for diabetes. The data is provided by three managed care organizations in Allegheny County (Gateway Health Plan, Highmark Health, and UPMC) and represents their insured population for the 2015 and calendar years. Disclaimer: Users should be cautious of using administrative claims data as a measure of disease prevalence and interpreting trends over time, as data provided were collected for purposes other than surveillance. Limitations of these data include but are not limited to: misclassification, duplicate individuals, exclusion of individuals who did not seek care in past two years and those who are: uninsured, enrolled in plans not represented in the dataset, or were not enrolled in one of the represented plans for at least 90 days.

Population-based county-level estimates for prevalence of DC were obtained from the Institute for Health Metrics and Evaluation (IHME) for the years 2004-2012 (16). DC prevalence rate was defined as the propor-tion of people within a county who had previously been diagnosed with diabetes (high fasting plasma glu-cose 126 mg/dL, hemoglobin A1c (HbA1c) of 6.5%, or diabetes diagnosis) but do not currently have high fasting plasma glucose or HbA1c for the period 2004-2012. DC prevalence estimates were calculated using a two-stage approach. The first stage used National Health and Nutrition Examination Survey (NHANES) data to predict high fasting plasma glucose (FPG) levels (≥126 mg/dL) and/or HbA1C levels (≥6.5% [48 mmol/mol]) based on self-reported demographic and behavioral characteristics (16). This model was then applied to Behavioral Risk Factor Surveillance System (BRFSS) data to impute high FPG and/or HbA1C status for each BRFSS respondent (16). The second stage used the imputed BRFSS data to fit a series of small area models, which were used to predict county-level prevalence of diabetes-related outcomes, including DC (16). The EQI was constructed for 2006-2010 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). Results are reported as prevalence rate differences (PRD) with 95% confidence intervals (CIs) comparing the highest quintile/worst environmental quality to the lowest quintile/best environmental quality expo-sure metrics. PRDs are representative of the entire period of interest, 2004-2012. Due to availability of DC data and covariate data, not all counties were captured, however, the majority, 3134 of 3142 were utilized in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., A. Krajewski, K. Price, D. Lobdell, and R. Sargis. Diabetes control is associated with environmental quality in the USA. Endocrine Connections. BioScientifica Ltd., Bristol, UK, 10(9): 1018-1026, (2021).

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of diabetes mellitus in persons (aged 17+). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to diabetes mellitus in persons (aged 17+).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (aged 17+) with diabetes mellitus was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population with diabetes mellitus was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with depression, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have diabetes mellitusB) the NUMBER of people within that MSOA who are estimated to have diabetes mellitusAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have diabetes mellitus, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from diabetes mellitus, and where those people make up a large percentage of the population, indicating there is a real issue with diabetes mellitus within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of diabetes mellitus, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of diabetes mellitus.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

Population-based county-level estimates for diagnosed (DDP), undiagnosed (UDP), and total diabetes prevalence (TDP) were acquired from the Institute for Health Metrics and Evaluation (IHME) for the years 2004-2012 (Evaluation 2017). Prevalence estimates were calculated using a two-stage approach. The first stage used National Health and Nutrition Examination Survey (NHANES) data to predict high fasting plasma glucose (FPG) levels (≥126 mg/dL) and/or hemoglobin A1C (HbA1C) levels (≥6.5% [48 mmol/mol]) based on self-reported demographic and behavioral characteristics (Dwyer-Lindgren, Mackenbach et al. 2016). This model was then applied to Behavioral Risk Factor Surveillance System (BRFSS) data to impute high FPG and/or A1C status for each BRFSS respondent (Dwyer-Lindgren, Mackenbach et al. 2016). The second stage used the imputed BRFSS data to fit a series of small area models, which were used to predict the county-level prevalence of each of the diabetes-related outcomes (Dwyer-Lindgren, Mackenbach et al. 2016). Diagnosed diabetes was defined as the proportion of adults (age 20+ years) who reported a previous diabetes diagnosis, represented as an age-standardized prevalence percentage. Undiagnosed diabetes was defined as proportion of adults (age 20+ years) who have a high FPG or HbA1C but did not report a previous diagnosis of diabetes. Total diabetes was defined as the proportion of adults (age 20+ years) who reported a previous diabetes diagnosis and/or had a high FPG/HbA1C. The age-standardized diabetes prevalence (%) was used as the outcome. The EQI was constructed for 2000-2005 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., A. Krajewski, S. Shaikh, D. Lobdell, and R. Sargis. Association between environmental quality and diabetes in the U.S.A.. Journal of Diabetes Investigation. John Wiley & Sons, Inc., Hoboken, NJ, USA, 11(2): 315-324, (2020).

Context:

Diabetes is a widespread chronic disease affecting millions of Americans each year, imposing a substantial financial burden on the economy. It impairs the body's ability to regulate blood glucose levels, leading to a range of health issues such as heart disease, vision loss, limb amputation, and kidney disease. Diabetes occurs when the body either fails to produce sufficient insulin or cannot use the insulin produced effectively. Insulin is crucial for enabling cells to utilize sugars from the bloodstream for energy.

Though there is no cure for diabetes, lifestyle changes such as weight management, healthy eating, and regular physical activity, along with medical treatments, can help manage the disease. Early detection and intervention are vital, making predictive models for diabetes risk valuable tools for healthcare providers and public health officials.

As of 2018, the CDC reported that 34.2 million Americans have diabetes, with 88 million having prediabetes. Alarmingly, a significant portion of those affected are unaware of their condition. Type II diabetes, the most prevalent form, varies in prevalence based on age, education, income, location, race, and other social determinants of health. The economic impact is substantial, with diagnosed diabetes costing approximately $327 billion annually, and total costs, including undiagnosed cases and prediabetes, nearing $400 billion.

Content: The dataset originates from the Behavioral Risk Factor Surveillance System (BRFSS), an annual telephone survey by the CDC since 1984, collecting data on health-related risk behaviors, chronic health conditions, and preventative service usage. For this project, the 2015 BRFSS dataset available on Kaggle was used, featuring responses from 441,455 individuals across 330 features.

The dataset includes three files:

1. diabetes_012_health_indicators_BRFSS2015.csv: Contains 253,680 responses with 21 features. The target variable, Diabetes_012, has 3 classes: 0 (no diabetes or only during pregnancy), 1 (prediabetes), and 2 (diabetes). This dataset is imbalanced.

2. diabetes_binary_5050split_health_indicators_BRFSS2015.csv: Contains 70,692 responses with 21 features, balanced 50-50 between individuals with no diabetes and those with prediabetes or diabetes. The target variable, Diabetes_binary, has 2 classes: 0 (no diabetes) and 1 (prediabetes or diabetes).

3. diabetes_binary_health_indicators_BRFSS2015.csv: Contains 253,680 responses with 21 features, with the target variable Diabetes_binary having 2 classes: 0 (no diabetes) and 1 (prediabetes or diabetes). This dataset is not balanced.

Research Questions: - Can BRFSS survey questions accurately predict diabetes? - What risk factors are most indicative of diabetes risk? - Can a subset of risk factors effectively predict diabetes risk? - Can a shorter questionnaire be developed from the BRFSS using feature selection to predict diabetes risk?

Acknowledgements: This dataset was not created by me; it is a cleaned and consolidated version of the BRFSS 2015 dataset available on Kaggle. The original dataset and the data cleaning notebook can be found here.

Inspiration: This work was inspired by Zidian Xie et al.'s study on building risk prediction models for Type 2 diabetes using machine learning techniques on the 2014 BRFSS dataset. The study can be found here.

1) Data introduction • Early-diabetes dataset contains 520 observations with 17 characteristics collected using direct questionnaires and diagnostic results from patients at Sylhet Diabetes Hospital, Sylhet, Bangladesh.

2) Data utilization (1) Early-diabetes data has characteristics that: • Predict diabetes risk using 16 tabular features, including age, gender, and polyuria. (2) Early-diabetes data can be used to: • Medical research: Researchers can use these data sets to study factors that contribute to the development of diabetes, identify key risk factors and help improve understanding of the disease. • Preventive healthcare: Using predictive models trained based on this data, healthcare providers can identify at-risk individuals early so they can intervene at the right time and create personalized treatment plans.

Decrease the percentage of people with Type 2 diabetes from 11.2% in 2014 to 10.1% by 2019.

Find data on pediatric diabetes in Massachusetts. This dataset contains information on the number of cases and prevalence of Type 1 and Type 2 diabetes among students, grades K-8, in Massachusetts.

ObjectiveAfrica presents a higher diabetic foot ulcer prevalence estimate of 7.2% against global figures of 6.3%. Engaging family members in self-care education interventions has been shown to be effective at preventing diabetes-related foot ulcers. This study culturally adapted and tested the feasibility and acceptability of an evidence-based footcare family intervention in Ghana.MethodsThe initial phase of the study involved stakeholder engagement, comprising Patient Public Involvement activities and interviews with key informant nurses and people with diabetes (N = 15). In the second phase, adults at risk of diabetes-related foot ulcers and nominated caregivers (N = 50 dyads) participated in an individually randomised feasibility trial of the adapted intervention (N = 25) compared to usual care (N = 25). The study aimed to assess feasibility outcomes and to identify efficacy signals on clinical outcomes at 12 weeks post randomisation. Patient reported outcomes were foot care behaviour, foot self-care efficacy, diabetes knowledge and caregiver diabetes distress.ResultsAdjustments were made to the evidence-based intervention to reflect the literacy, information needs and preferences of stakeholders and to develop a context appropriate diabetic foot self-care intervention. A feasibility trial was then conducted which met all recruitment, retention, data quality and randomisation progression criteria. At 12 weeks post randomisation, efficacy signals favoured the intervention group on improved footcare behaviour, foot self-care efficacy, diabetes knowledge and reduced diabetes distress. Future implementation issues to consider include the staff resources needed to deliver the intervention, family members availability to attend in-person sessions and consideration of remote intervention delivery.ConclusionA contextual family-oriented foot self-care education intervention is feasible, acceptable, and may improve knowledge and self-care with the potential to decrease diabetes-related complications. The education intervention is a strategic approach to improving diabetes care and prevention of foot disease, especially in settings with limited diabetes care resources. Future research will investigate the possibility of remote delivery to better meet patient and staff needs.Trial registrationPan African Clinical Trials Registry (PACTR) ‐ PACTR202201708421484: https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=19363 or pactr.samrc.ac.za/Search.aspx.

This data set provides de-identified population data for diabetes and hypertension comorbidity prevalence in Allegheny County. The data is provided by three managed care organizations in Allegheny County (Gateway Health Plan, Highmark Health, and UPMC) and represents their insured population for the 2015 and 2016 calendar years. Disclaimer: Users should be cautious of using administrative claims data as a measure of disease prevalence and interpreting trends over time, as data provided were collected for purposes other than surveillance. Limitations of these data include but are not limited to: misclassification, duplicate individuals, exclusion of individuals who did not seek care in past two years and those who are: uninsured, enrolled in plans not represented in the dataset, or were not enrolled in one of the represented plans for at least 90 days.

Note: This dataset has been archived as of January 2024 after confirmation from NHS Digital that the source dataset is no longer being updated, and there is not a replacement publication for the diabetic ketoacidosis admissions data. This indicator is one measure of the prevention, identification and management of people at risk of developing diabetes and those with the condition. It shows adverse outcomes as annual numbers of emergency hospital admissions for diabetic ketoacidosis and coma. Emergency admissions to hospital can be avoided by identifying people at risk, primary care services interventions, encouraging better diet and exercise, improving self-monitoring and diabetes control and supporting patients and carers in the management of diabetes in the home. It needs local health and care services working effectively together to support people’s health and independence in the community. Type 2 diabetes (around 90 percent of diabetes diagnoses) is partially preventable - it can be prevented or delayed by lifestyle changes (exercise, weight loss, healthy eating). Earlier detection of type 2 diabetes followed by effective treatment reduces the risk of developing diabetic complications. These include cardiovascular, kidney, foot and eye diseases, meaning considerable illness and reduced quality of life. There are some limitations to this data, as raw counts of hospital episodes are subject to population structures (such as numbers of people in older age groups) and other underlying variations. Counts below 5 are removed from the data. The data is updated annually. Sources: NHS Digital (now part of NHS England) - dataset P02177, and commentary from the Office for Health Improvement and Disparities (OHID) Public Health Outcomes Framework (PHOF) indicator 2.17 Recorded Diabetes.

IntroductionType 2 diabetes (T2D) is a growing public health burden throughout the world. Many people looking for information on how to prevent T2D will search on diabetes websites. Multiple dietary factors have a significant association with T2D risk, such as high intake of added sugars, refined carbohydrates, saturated fat, and red meat or processed meat; and decreased intake of dietary fiber, and fruits/vegetables. Despite this dietary information being available in the scientific literature, it is unclear whether this information is available in gray literature (websites).ObjectiveIn this study, we evaluate the use of specific terms from diabetes websites that are significantly associated with causes/risk factors and preventions for T2D from three term categories: (A) dietary factors, (B) nondietary nongenetic (lifestyle-associated) factors, and (C) genetic (non-modifiable) factors. We also evaluate the effect of website type (business, government, nonprofit) on term usage among websites.MethodsWe used web scraping and coding tools to quantify the use of specific terms from 73 diabetes websites. To determine the effect of term category and website type on the usage of specific terms among 73 websites, a repeated measures general linear model was performed.ResultsWe found that dietary risk factors that are significantly associated with T2D (e.g., sugar, processed carbohydrates, dietary fat, fruits/vegetables, fiber, processed meat/red meat) were mentioned in significantly fewer websites than either nondietary nongenetic factors (e.g., obesity, physical activity, dyslipidemia, blood pressure) or genetic factors (age, family history, ethnicity). Among websites that provided “eat healthy” guidance, one third provided zero dietary factors associated with type 2 diabetes, and only 30% provided more than two specific dietary factors associates with type 2 diabetes. We also observed that mean percent usage of all terms associated with T2D causes/risk factors and preventions was significantly lower among government websites compared to business websites and nonprofit websites.ConclusionDiabetes websites need to increase their usage of dietary factors when discussing causes/risk factors and preventions for T2D; as dietary factors are modifiable and strongly associated with all nondietary nongenetic risk factors, in addition to T2D risk.

The Diabetes Dataset is commonly used for predictive modeling and medical research, aiming to analyze factors contributing to diabetes and predict its occurrence based on various health metrics. - Pregnancies – More pregnancies may increase the risk of gestational diabetes, which can lead to Type 2 diabetes.
- Glucose – High blood sugar levels indicate poor insulin regulation, a key factor in diabetes.
- Skin Thickness – Measures subcutaneous fat; higher values may indicate insulin resistance.
- Insulin – Low or high insulin levels can signal improper glucose metabolism, leading to diabetes.
- Blood Pressure (BP) – Hypertension is often linked with insulin resistance and diabetes complications.
- Diabetes Pedigree Function – Estimates genetic predisposition to diabetes based on family history.
- Age – Older individuals have a higher risk of developing Type 2 diabetes.
- BMI (Body Mass Index) – High BMI is associated with obesity, a major risk factor for diabetes.

Would you like further insights on how these factors interact in diabetes prediction? 😊

Diabetes is a chronic disease that affects the way the body processes blood sugar, also known as glucose. Glucose is an important source of energy for the body's cells, and insulin, a hormone produced by the pancreas, helps to regulate glucose levels in the blood.

In people with diabetes, the body either doesn't produce enough insulin, or it can't effectively use the insulin it produces. This causes glucose to build up in the blood, leading to a range of health problems over time.

There are two main types of diabetes: type 1 and type 2. Type 1 diabetes, also known as juvenile diabetes, is usually diagnosed in children and young adults. It occurs when the body's immune system attacks and destroys the cells in the pancreas that produce insulin. People with type 1 diabetes need to take insulin injections or use an insulin pump to manage their blood sugar levels.

Type 2 diabetes is the most common form of diabetes, accounting for around 90% of all cases. It usually develops in adults, but can also occur in children and teenagers. In type 2 diabetes, the body becomes resistant to the effects of insulin, and the pancreas may not produce enough insulin to keep blood sugar levels in check. Lifestyle changes, such as a healthy diet and regular exercise, can help manage type 2 diabetes, and some people may also need medication or insulin therapy.

Both types of diabetes can lead to serious health complications over time, including heart disease, stroke, kidney disease, nerve damage, and eye problems. It's important for people with diabetes to work closely with their healthcare team to manage their condition and prevent these complications.

دیابت بیماری مزمنی است که نحوه پردازش قند خون را در بدن تحت تأثیر قرار می‌دهد. قند یک منبع مهم انرژی برای سلول‌های بدن است و انسولین، یک هورمون توسط پانکراس تولید شده، به کنترل سطح قند خون در بدن کمک می‌کند

در افراد دیابتی، بدن یا انسولین کافی تولید نمی‌کند یا نمی‌تواند به طور موثر از انسولینی که تولید می‌شود، استفاده کند. این باعث می‌شود که قند در خون تجمع پیدا کند که به مشکلات سلامتی در طول زمان منجر می‌شود

دو نوع اصلی دیابت وجود دارد: نوع ۱ و نوع ۲. دیابت نوع ۱ یا دیابت جوانان، معمولاً در کودکان و جوانان بزرگسال تشخیص داده می‌شود. این بیماری زمانی رخ می‌دهد که سیستم ایمنی بدن سلول‌های پانکراسی را که انسولین تولید می‌کنند، حمله می‌کند و از بین می‌برد. افراد دیابتی نوع ۱ باید تزریقات انسولین یا استفاده از پمپ انسولین برای کنترل سطح قند خون خود استفاده کنند

دیابت نوع ۲ شایع‌ترین نوع دیابت است که حدود ۹۰٪ از کل موارد را شامل می‌شود. این نوع بیماری معمولاً در بزرگسالان ایجاد می‌شود، اما ممکن است در کودکان و نوجوانان نیز رخ دهد. در دیابت نوع ۲، بدن به اثرات انسولین مقاومت پیدا می‌کند و پانکراس ممکن است انسولین کافی برای کنترل سطح قند خون تولید نکند. تغییرات سبک زندگی مانند رژیم غذایی سالم و ورزش منظم می‌تواند به مدیریت دیابت نوع ۲ کمک کند و برخی افراد ممکن است نیاز به دارو یا درمان انسولین داشته باشند

هر دو نوع دیابت می‌تواند منجر به مشکلات سلامتی جدی در طول زمان شود، از جمله بیماری قلبی، سکته مغزی، بیماری کلیه، آسیب عصبی و مشکلات چشمی. برای افراد دارای دیابت، مهم است که به همراه تیم مراقبت از سلامتی خود همکاری کرده و برای جلوگیری از این مشکلات تلاش کنند

Diabetes ist eine chronische Krankheit, die die Art und Weise beeinflusst, wie der Körper Blutzucker, auch als Glukose, verarbeitet. Glukose ist eine wichtige Energiequelle für die Zellen des Körpers, und Insulin, ein Hormon, das von der Bauchspeicheldrüse produziert wird, hilft bei der Regulierung des Glukosespiegels im Blut.

Bei Menschen mit Diabetes produziert der Körper entweder nicht genug Insulin oder kann das Insulin, das er produziert, nicht effektiv nutzen. Dies führt dazu, dass sich Glukose im Blut ansammelt, was im Laufe der Zeit zu einer Reihe von Gesundheitsproblemen führen kann.

Es gibt zwei Haupttypen von Diabetes: Typ 1 und Typ 2. Diabetes Typ 1, auch als juveniler Diabetes bekannt, wird in der Regel bei Kindern und jungen Erwachsenen diagnostiziert. Es tritt auf, wenn das Immunsystem des Körpers die Zellen in der Bauchspeicheldrüse angreift und zerstört, die Insulin produzieren. Menschen mit Diabetes Typ 1 müssen Insulininjektionen oder eine Insulinpumpe verwenden, um ihren Blutzuckerspiegel zu kontrollieren.

Diabetes Typ 2 ist die häufigste Form von Diabetes und macht etwa 90% aller Fälle aus. Es entwickelt sich in der Regel bei Erwachsenen, kann aber auch bei Kindern und Jugendlichen auftreten. Bei Diabetes Typ 2 wird der Körper gegenüber den Wirkungen von Insulin resistent, und die Ba...

BackgroundData on the future diabetes burden in Scandinavia is limited. Our aim was to project the future burden of diabetes in Sweden by modelling data on incidence, prevalence, mortality, and demographic factors.MethodTo project the future burden of diabetes we used information on the prevalence of diabetes from the national drug prescription registry (adults ≥20 years), previously published data on relative mortality in people with diabetes, and population demographics and projections from Statistics Sweden. Alternative scenarios were created based on different assumptions regarding the future incidence of diabetes.ResultsBetween 2007 and 2013 the prevalence of diabetes rose from 5.8 to 6.8% in Sweden but incidence remained constant at 4.4 per 1000 (2013). With constant incidence and continued improvement in relative survival, prevalence will increase to 10.4% by year 2050 and the number of afflicted individuals will increase to 940 000. Of this rise, 30% is accounted for by changes in the age structure of the population and 14% by improved relative survival in people with diabetes. A hypothesized 1% annual rise in incidence will result in a prevalence of 12.6% and 1 136 000 cases. Even with decreasing incidence at 1% per year, prevalence of diabetes will continue to increase.ConclusionWe can expect diabetes prevalence to rise substantially in Sweden over the next 35 years as a result of demographic changes and improved survival among people with diabetes. A dramatic reduction in incidence is required to prevent this development.

South Africa ZA: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 5.520 % in 2017. South Africa ZA: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 5.520 % from Dec 2017 (Median) to 2017, with 1 observations. South Africa ZA: Diabetes Prevalence: % of Population Aged 20-79 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Health Statistics. Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes.; ; International Diabetes Federation, Diabetes Atlas.; Weighted average;

The National Diabetes Audit (NDA) provides a comprehensive view of diabetes care in England and Wales. It measures the effectiveness of diabetes healthcare against NICE Clinical Guidelines and NICE Quality Standards. This is the Type 1 Diabetes report. It details the findings and recommendations relating to diabetes care process completion, treatment target achievement and structured education for people with type 1 diabetes aged 19 years and over.