West Virginia, Mississippi, and Arkansas are the U.S. states with the highest percentage of their population who are obese. The states with the lowest percentage of their population who are obese include Colorado, Hawaii, and Massachusetts. Obesity in the United States Obesity is a growing problem in many countries around the world, but the United States has the highest rate of obesity among all OECD countries. The prevalence of obesity in the United States has risen steadily over the previous two decades, with no signs of declining. Obesity in the U.S. is more common among women than men, and overweight and obesity rates are higher among African Americans than any other race or ethnicity. Causes and health impacts Obesity is most commonly the result of a combination of poor diet, overeating, physical inactivity, and a genetic susceptibility. Obesity is associated with various negative health impacts, including an increased risk of cardiovascular diseases, certain types of cancer, and diabetes type 2. As of 2022, around 8.4 percent of the U.S. population had been diagnosed with diabetes. Diabetes is currently the eighth leading cause of death in the United States.

The adult obesity rate, or the percentage of the county population (age 18 and older*) that is obese, or has a Body Mass Index (BMI) equal to or greater than 30 [kg/m2], is illustrative of a serious health problem, in Champaign County, statewide, and nationally.

The adult obesity rate data shown here spans from Reporting Years (RY) 2015 to 2024. Champaign County’s adult obesity rate fluctuated during this time, peaking in RY 2022. The adult obesity rates for Champaign County, Illinois, and the United States were all above 30% in RY 2024, but the Champaign County rate was lower than the state and national rates. All counties in Illinois had an adult obesity rate above 30% in RY 2024, but Champaign County's rate is one of the lowest among all Illinois counties.

Obesity is a health problem in and of itself, and is commonly known to exacerbate other health problems. It is included in our set of indicators because it can be easily measured and compared between Champaign County and other areas.

This data was sourced from the University of Wisconsin’s Population Health Institute’s and the Robert Wood Johnson Foundation’s County Health Rankings & Roadmaps. Each year’s County Health Rankings uses data from the most recent previous years that data is available. Therefore, the 2024 County Health Rankings (“Reporting Year” in the table) uses data from 2021 (“Data Year” in the table). The survey methodology changed in Reporting Year 2015 for Data Year 2011, which is why the historical data shown here begins at that time. No data is available for Data Year 2018. The County Health Rankings website notes to use caution if comparing RY 2024 data with prior years.

*The percentage of the county population measured for obesity was age 20 and older through Reporting Year 2021, but starting in Reporting Year 2022 the percentage of the county population measured for obesity was age 18 and older.

Source: University of Wisconsin Population Health Institute. County Health Rankings & Roadmaps 2024. www.countyhealthrankings.org.

**Citation Request: ** Koklu, N., & Sulak, S.A. (2024). Using artificial intelligence techniques for the analysis of obesity status according to the individuals' social and physical activities. Sinop Üniversitesi Fen Bilimleri Dergisi, 9(1), 217-239. https://doi.org/10.33484/sinopfbd.1445215

Obesity Dataset

Obesity is a serious and chronic disease with genetic and environmental interactions. It is defined as an excessive amount of fat tissue in the body that is harmful to health. The main risk factors for obesity include social, psychological, and eating habits. Obesity is a significant health problem for all age groups in the world. Currently, more than 2 billion people worldwide are obese or overweight. Research has shown that obesity can be prevented. In this study, artificial intelligence methods were used to identify individuals at risk of obesity. An online survey was conducted on 1610 individuals to create the obesity dataset. To analyze the survey data, four commonly used artificial intelligence methods in literature, namely Artificial Neural Network, K Nearest Neighbors, Random Forest and Support Vector Machine, were employed after pre-processing. As a result of this analysis, obesity classes were predicted correctly with success rates of 74.96%, 74.03%, 74.03% and 87.82%, respectively. Random Forest was the most successful artificial intelligence method for this dataset and accurately classified obesity with a success rate of 87.82%.

Attributes/Values Sex Male (712) Female (898)

Age Values in integers

Height Values in integers (cm)

Overweight/Obese Families Yes (266) No (1344)

Consumption of Fast Food Yes (436) No (1174)

Frequency of Consuming Vegetables Rarely (400) Sometimes (708) Always (502)

Number of Main Meals Daily 1. 1-2 (444) 3 (928) 3+ (238)

Food Intake Between Meals Rarely (346) Sometimes (564) Usually (417) Always (283)

Smoking Yes (492) No (1118)

Liquid Intake Daily amount smaller than one liter (456) Within the range of 1 to 2 liters (523) In excess of 2 liters (631)

Calculation Of Calorie Intake Yes (286) No (1324)

Physical Exercise No physical activity (206) In the range of 1-2 days (290) In the range of 3-4 days (370) In the range of 5-6 days (358) 6+ days (386)

Schedule Dedicated to Technology Between 0 and 2 hours (382) Between 3 and 5 hours (826) Exceeding five hours (402)

Type of Transportation Used Automobile (660) Motorbike (94) Bike (116) Public transportation (602) Walking (138)

Class Underweight (73) Normal (658) Overweight (592) Obesity (287)

Data on normal weight, overweight, and obesity among adults aged 20 and over by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. SOURCE: NCHS, National Health and Nutrition Examination Survey. For more information on the National Health and Nutrition Examination Survey, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.

These data are from the 2013 California Dietary Practices Surveys (CDPS), 2012 California Teen Eating, Exercise and Nutrition Survey (CalTEENS), and 2013 California Children’s Healthy Eating and Exercise Practices Surveys (CalCHEEPS). These surveys have been discontinued. Adults, adolescents, and children (with parental assistance) were asked for their current height and weight, from which, body mass index (BMI) was calculated. For adults, a BMI of 30.0 and above is considered obese. For adolescents and children, obesity is defined as having a BMI at or above the 95th percentile, according to CDC growth charts.

The California Dietary Practices Surveys (CDPS), the California Teen Eating, Exercise and Nutrition Survey (CalTEENS), and the California Children’s Healthy Eating and Exercise Practices Surveys (CalCHEEPS) (now discontinued) were the most extensive dietary and physical activity assessments of adults 18 years and older, adolescents 12 to 17, and children 6 to 11, respectively, in the state of California. CDPS and CalCHEEPS were administered biennially in odd years up through 2013 and CalTEENS was administered biennially in even years through 2014. The surveys were designed to monitor dietary trends, especially fruit and vegetable consumption, among Californias for evaluating their progress toward meeting the Dietary Guidelines for Americans and the Healthy People 2020 Objectives. All three surveys were conducted via telephone. Adult and adolescent data were collected using a list of participating CalFresh households and random digit dial, and child data were collected using only the list of CalFresh households. Older children (9-11) were the primary respondents with some parental assistance. For younger children (6-8), the primary respondent was parents. Data were oversampled for low-income and African American to provide greater sensitivity for analyzing trends among the target population. Wording of the question used for these analyses varied by survey (age group). The questions were worded are as follows: Adult:1) How tall are you without shoes?2) How much do you weigh?Adolescent:1) About how much do you weigh without shoes?2) About how tall are you without shoes? Child:1) How tall is [child's name] now without shoes on?2) How much does [child's name] weigh now without shoes on?

State of Illinois Obesity Percentages by County. Explanation of field attributes: Obesity - The percent of each Illinois county’s population that is considered obese from the 2015 CDC BRFSS Survey.

United States US: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data was reported at 6.900 % in 2012. This records an increase from the previous number of 6.400 % for 2009. United States US: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 6.900 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 8.700 % in 2005 and a record low of 5.100 % in 1991. United States US: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 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. Prevalence of overweight, female, is the percentage of girls under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

This dataset includes data on adolescent's diet, physical activity, and weight status from Youth Risk Behavior Surveillance System (YRBSS). This data is used for DNPAO's Data, Trends, and Maps database, which provides national and state specific data on obesity, nutrition, physical activity, and breastfeeding. For more information about YRBSS visit https://www.cdc.gov/healthyyouth/data/yrbs/index.htm.

This dataset includes select data from the U.S. Census Bureau's American Community Survey (ACS) on the percent of adults who bike or walk to work. This data is used for DNPAO's Data, Trends, and Maps database, which provides national and state specific data on obesity, nutrition, physical activity, and breastfeeding. For more information about ACS visit https://www.census.gov/programs-surveys/acs/.

The prevalence of obesity in the adult population (18 years and older) in Trinidad and Tobago. Overweight and obesity are defined as abnormal or excessive fat accumulation that may impair health. Body mass index (BMI) is a simple weight-for-height index commonly used to classify overweight and obesity in adults. It is defined as a person's weight in kilograms divided by the square of his height in meters (kg/m2). Adults For adults, the World Health Organization (WHO) defines overweight and obesity as follows: overweight is a BMI greater than or equal to 25; and obesity is a BMI greater than or equal to 30. BMI provides the most useful population-level measure of overweight and obesity as it is the same for both sexes and all ages of adults. However, it should be considered a rough guide because it may not correspond to the same degree of fatness in different individuals. The Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC-SA 3.0 IGO) specifies that you must give appropriate attribution and credit to FAO for any work produced using a dataset or when data is re-disseminated. The following citation is recommended: [© FAO] [Year of publication] [Title of content] [Page number (for publications)] [Location on FAO website] [Date accessed and/or downloaded] Example: © FAO 2023, Prevalence of Moderate and Severe Food Insecurity, https://www.fao.org/faostat/en/#country/220, Accessed on November 21, 2023

Adolescents Who Have Obesity - "This indicator shows the percentage of adolescent public high school students who are obese.

In the last 20 years, the percentage of overweight/obese children has more than doubled and, for adolescents, it has tripled. Overweight/obese children are at increased risk of developing life-threatening chronic diseases, such as Type 2 diabetes." https://health.maryland.gov/pophealth/Documents/SHIP/SHIP%20Lite%20Data%20Details/Adolescents%20who%20have%20Obesity.pdf" > Link to Data Details

This dataset includes breastfeeding data from the National Immunization Survey (NIS). This data is used for DNPAO's Data, Trends, and Maps database, which provides national and state specific data on obesity, nutrition, physical activity, and breastfeeding. For more information about breastfeeding and NIS visit https://www.cdc.gov/breastfeeding/data/nis_data/index.htm.

This dataset includes data on weight status for children aged 3 months to 4 years old from Women, Infant, and Children Participant and Program Characteristics (WIC-PC). This data is used for DNPAO's Data, Trends, and Maps database, which provides national and state specific data on obesity, nutrition, physical activity, and breastfeeding. For more information about WIC-PC visit https://www.fns.usda.gov/wic/national-survey-wic-participants.

The share of the population with overweight in Canada was forecast to continuously increase between 2024 and 2029 by in total 1.6 percentage points. After the fifteenth consecutive increasing year, the overweight population share is estimated to reach 74.45 percent and therefore a new peak in 2029. Notably, the share of the population with overweight of was continuously increasing over the past years.Overweight is defined as a body mass index (BMI) of more than 25.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the share of the population with overweight in countries like Mexico and United States.

This dataset includes data on weight status for children aged 3 months to 4 years old from Women, Infant, and Children Participant and Program Characteristics (WIC-PC). This data is used for DNPAO's Data, Trends, and Maps database, which provides national and state specific data on obesity, nutrition, physical activity, and breastfeeding. For more information about WIC-PC visit https://www.fns.usda.gov/wic/national-survey-wic-participants.

This subset of the community health indicator report data will not be updated. A dataset containing all of the community health indicators is now available. To view the latest community health obesity and diabetes related indicators, see the featured content section. This Obesity and Diabetes Related Indicators dataset provides a subset of data (40 indicators) for the two topics: Obesity and Diabetes. The dataset includes percentage or rate for Cirrhosis/Diabetes and Obesity and Related Indicators, where available, for all counties, regions and state.
New York State Community Health Indicator Reports (CHIRS) were developed in 2012, and annually updated to provide data for over 300 health indicators, organized by 15 health topic and data for all counties, regions and state are presented in table format with links to trend graphs and maps. Most recent county and state level data are provided. Multiple year combined data offers stable estimates for the burden and risk factors for these two health topics.

This Obesity and Diabetes Related Indicators dataset provides a subset of data (40 indicators) for the two topics: Obesity and Diabetes. The dataset includes percentage or rate for Cirrhosis/Diabetes and Obesity and Related Indicators, where available, for all counties, regions and state.
New York State Community Health Indicator Reports (CHIRS) were developed in 2012, and annually updated to provide data for over 300 health indicators, organized by 15 health topic and data for all counties, regions and state are presented in table format with links to trend graphs and maps (http://www.health.ny.gov/statistics/chac/indicators/). Most recent county and state level data are provided. Multiple year combined data offers stable estimates for the burden and risk factors for these two health topics. For more information, check out: http://www.health.ny.gov/statistics/chac/indicators/ or go to the “About” tab.

BackgroundIn India, the prevalence of overweight and obesity has increased rapidly in recent decades. Given the association between overweight and obesity with many non-communicable diseases, forecasts of the future prevalence of overweight and obesity can help inform policy in a country where around one sixth of the world’s population resides.MethodsWe used a system of multi-state life tables to forecast overweight and obesity prevalence among Indians aged 20–69 years by age, sex and urban/rural residence to 2040. We estimated the incidence and initial prevalence of overweight using nationally representative data from the National Family Health Surveys 3 and 4, and the Study on global AGEing and adult health, waves 0 and 1. We forecasted future mortality, using the Lee-Carter model fitted life tables reported by the Sample Registration System, and adjusted the mortality rates for Body Mass Index using relative risks from the literature.ResultsThe prevalence of overweight will more than double among Indian adults aged 20–69 years between 2010 and 2040, while the prevalence of obesity will triple. Specifically, the prevalence of overweight and obesity will reach 30.5% (27.4%-34.4%) and 9.5% (5.4%-13.3%) among men, and 27.4% (24.5%-30.6%) and 13.9% (10.1%-16.9%) among women, respectively, by 2040. The largest increases in the prevalence of overweight and obesity between 2010 and 2040 is expected to be in older ages, and we found a larger relative increase in overweight and obesity in rural areas compared to urban areas. The largest relative increase in overweight and obesity prevalence was forecast to occur at older age groups.ConclusionThe overall prevalence of overweight and obesity is expected to increase considerably in India by 2040, with substantial increases particularly among rural residents and older Indians. Detailed predictions of excess weight are crucial in estimating future non-communicable disease burdens and their economic impact.

Data on obesity among children and adolescents aged 2-19 years by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://res1wwwd-o-tcdcd-o-tgov.vcapture.xyz/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. SOURCE: NCHS, National Health and Nutrition Examination Survey. For more information on the National Health and Nutrition Examination Survey, see the corresponding Appendix entry at https://res1wwwd-o-tcdcd-o-tgov.vcapture.xyz/nchs/data/hus/hus19-appendix-508.pdf.

Cameroon CM: Prevalence of Overweight: Weight for Height: Male: % of Children Under 5 data was reported at 12.200 % in 2018. This records an increase from the previous number of 7.100 % for 2014. Cameroon CM: Prevalence of Overweight: Weight for Height: Male: % of Children Under 5 data is updated yearly, averaging 8.200 % from Dec 1991 (Median) to 2018, with 7 observations. The data reached an all-time high of 12.200 % in 2018 and a record low of 6.000 % in 1991. Cameroon CM: Prevalence of Overweight: Weight for Height: Male: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cameroon – Table CM.World Bank.WDI: Social: Health Statistics. Prevalence of overweight, male, is the percentage of boys under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.;;Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.