In 2023, the states with the highest death rates due to heart disease were Oklahoma, Mississippi, and Alabama. That year, there were around 251 deaths due to heart disease per 100,000 population in the state of Oklahoma. In comparison, the overall death rate from heart disease in the United States was 162 per 100,000 population. The leading cause of death in the United States Heart disease is the leading cause of death in the United States, accounting for 22 percent of all deaths in 2023. That year, cancer was the second leading cause of death, followed by unintentional injuries and cerebrovascular diseases. In the United States, a person has a one in six chance of dying from heart disease. Death rates for heart disease are higher among men than women, but both have seen steady decreases in heart disease death rates since the 1950s. What are risk factors for heart disease? Although heart disease is the leading cause of death in the United States, the risk of heart disease can be decreased by avoiding known risk factors. Some of the leading preventable risk factors for heart disease include smoking, heavy alcohol use, physical inactivity, an unhealthy diet, and being overweight or obese. It is no surprise that the states with the highest rates of death from heart disease are also the states with the highest rates of heart disease risk factors. For example, Oklahoma, the state with the highest heart disease death rate, is also the state with the sixth-highest rate of obesity. Furthermore, Mississippi is the state with the highest levels of physical inactivity, and it has the second-highest heart disease death rate in the United States.

The number of deaths caused by heart disease has decreased in the United States from ***** per 100,000 population in 1990 to ***** deaths per 100,000 population in 2019. Nevertheless, heart disease is still the leading cause of death in the country, followed closely by cancer, which has a mortality rate of ***** per 100,000 people. Heart disease in the U.S.Diseases of the heart and blood vessels are often associated with atherosclerosis, which occurs when plaque builds up along arterial walls. This can limit the flow of blood and can lead to blood clots, a common cause of stroke or heart attacks. Other types of heart disease include arrhythmia (abnormal heart rhythms) and heart valve problems. Many of these diseases can be treated with medication, although many complications will still remain. One of the leading cholesterol lowering drugs in the United States, Crestor, generated around **** billion U.S. dollars of revenue in 2024. Risk Factors for heart disease There are many risk factors associated with the development of heart disease, including family history, ethnicity, and age. However, there are other factors that can be modified through lifestyle changes such as physical inactivity, smoking, and unhealthy diets. Obesity has also been commonly associated with risk factors like hypertension and diabetes type II. In the United States, some ** percent of white adults are currently obese.

Death rate has been age-adjusted to the 2000 U.S. standard population. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Coronary heart disease is a type of heart disease in which the arteries of the heart cannot deliver enough oxygen-rich blood to the heart muscles. Over time, this can weaken the heart muscle and may lead to heart attack or heart failure. It is the most common type of heart disease in the US and has been the leading cause of death in Los Angeles County for the last two decades. Poor diet, sedentary lifestyle, tobacco exposure, and chronic stress are all important risk factors for coronary heart disease. Cities and communities can mitigate these risks by improving local food environments and encouraging physical activity by making communities safer and more walkable.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Key Indicators of Heart Disease

2022 annual CDC survey data of 400k+ adults related to their health status

What subject does the dataset cover?

According to the CDC, heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect "patterns" in the data that can predict a patient's condition.

Where did the data set come from and what treatments has it undergone?

The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the CDC: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.

What can you do with this data set?

As described above, the original dataset of nearly 300 variables was reduced to 40variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable "HadHeartAttack" as binary ("Yes" - respondent had heart disease; "No" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?

What steps did you use to convert the dataset?

Check out this notebook in my GitHub repository: https://github.com/kamilpytlak/data-science-projects/blob/main/heart-disease-prediction/2022/notebooks/data_processing.ipynb

Heart Disease is among the most prevalent chronic diseases in the United States, impacting millions of Americans each year and exerting a significant financial burden on the economy. In the United States alone, heart disease claims roughly 647,000 lives each year — making it the leading cause of death. The buildup of plaques inside larger coronary arteries, molecular changes associated with aging, chronic inflammation, high blood pressure, and diabetes are all causes of and risk factors for heart disease. While there are different types of coronary heart disease, the majority of individuals only learn they have the disease following symptoms such as chest pain, a heart attack, or sudden cardiac arrest. This fact highlights the importance of preventative measures and tests that can accurately predict heart disease in the population prior to negative outcomes like myocardial infarctions (heart attacks) taking place

The leading causes of death in the United States are, by far, cardiovascular diseases and cancer. However, the death rates from these diseases, as well as other leading causes of death, have decreased over the past few decades. The one major exception is deaths caused by Alzheimer’s disease, which have increased significantly. Cardiovascular disease deaths Although cardiovascular diseases are currently the leading cause of death in the United States, the death rate of these diseases has dropped significantly. In the year 1950, there were around *** deaths per 100,000 population due to cardiovascular diseases. In the year 2023, this number was ***** per 100,000 population. Risk factors for heart disease include smoking, poor diet, diabetes, obesity, stress, family history, and age. Alzheimer’s disease deaths While the death rates for cardiovascular disease, cancer, diabetes, and chronic lower respiratory diseases have all decreased, the death rate for Alzheimer’s disease has increased. In fact, from the year 2000 to 2022, the death rate from Alzheimer’s disease rose an astonishing *** percent. This increase is in part due to a growing aging population.

Heart Disease & Stroke Risk Factors

Modifiable Risk Factors in the United States

By Centers for Disease Control and Prevention [source]

About this dataset

The Behavioral Risk Factors for Heart Disease & Stroke (BRFSS) dataset is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death. This dataset provides an unprecedented opportunity to understand the public health burden of cardiovascular disease in the United States to present. It provides important indicators such as heart failure and risk factors like hypertension that can be used to analyze trends and stratify by age group, sex, race/ethnicity, and more. Through this data source we are able to get a well-rounded view of modifiable risks associated with illness related to heart disease and stroke throughout all 50 states. This information is imperative in understanding current health concerns across America and producing meaningful strategies in achieving healthier communities

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How to use the dataset

This dataset offers valuable insights into the modifiable risk factors associated with Heart Disease & Stroke in the United States. In order to effectively use this dataset, it is important to understand what each of the columns represent and how they are used to gain insight into this important public health issue.

Research Ideas

• Establishing and tracking modifiable health risk factors in different areas to create targeted public health campaigns
• Connecting data from this dataset with socio-demographic and economic factors to determine the impact of disparities on modifiable risk factors associated with heart disease and stroke
• Analyze trends in CVDs and associated risk factors such as hypertension or diabetes across multiple locations to better understand how they differ

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: Behavioral_Risk_Factor_Data_Heart_Disease_Stroke_Prevention.csv | Column name | Description | |:-------------------------------|:------------------------------------------------------------------------| | Year | The year the data was collected. (Integer) | | LocationAbbr | The abbreviation of the location where the data was collected. (String) | | LocationDesc | The description of the location where the data was collected. (String) | | Datasource | The source of the data. (String) | | PriorityArea1 | The first priority area of the data. (String) | | PriorityArea2 | The second priority area of the data. (String) | | PriorityArea3 | The third priority area of the data. (String) | | PriorityArea4 | The fourth priority area of the data. (String) | | Category | The category of the data. (String) | | Topic | The topic of the data. (String) | | Indicator | The indicator of the data. (String) | | Break_Out_Category | The break out category of the data. (String) | | Break_out | The break out of the data. (String) | | Data_Value_Type | The type of data value. (String) | | Data_Value_Unit | The unit of the data value. (String) | | Data_Value_Footnote_Symbol | The footnote symbol of the data value. (String) | | GeoLocation | The geographic location of the data. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Centers for Disease Control and Prevention.

2011 to present. BRFSS is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death. Indicators from this data source have been computed by personnel in CDC's Division for Heart Disease and Stroke Prevention (DHDSP). This is one of the datasets provided by the National Cardiovascular Disease Surveillance System. The system is designed to integrate multiple indicators from many data sources to provide a comprehensive picture of the public health burden of CVDs and associated risk factors in the United States. The data are organized by location (national, regional, state, and selected sites) and indicator, and they include CVDs (e.g., heart failure) and risk factors (e.g., hypertension). The data can be plotted as trends and stratified by age group, sex, and race/ethnicity.

Heart disease and cancer were the leading causes of death in the United States in 2023. COVID-19 became the third leading cause of death in 2020 and 2021, but by 2023 it was the tenth leading cause. This statistic shows the rates of the 10 leading causes of death in the United States in 2023.

According to Cognitive Market Research, the Global Heart Attack Diagnostics Market Size was USD XX Billion in 2023 and is set to achieve a market size of USD XX Billion by the end of 2031 growing at a CAGR of XX% from 2024 to 2031.

The Heart Attack Diagnostics market will expand significantly by XX% CAGR between 2024 and 2031.
The non-invasive heart attack diagnostics type accounts for the largest market share and is anticipated to a healthy growth over the approaching years.
The Electrocardiogram Heart Attack Diagnostics holds the largest market share compared to others.
The usage of Heart Attack Diagnostics by hospitals & clinics as end-users holds the largest market share compared to others.
North-America region dominated the market and accounted for the highest revenue of XX% in 2022 and it is projected that it will grow at a CAGR of XX% in the future.

Factors Affecting the Heart Attack Diagnostics Market

The unhealthy lifestyles among people are raising heart attacks and related diseases.

The common sedentary lifestyle and poor nutrition are the main causes of heart attacks. It is revealed that youths who lead modern lifestyles are more likely to suffer from heart attacks. Poor food choices like fast food, processed foods, and sugary drinks are all increasing the risk of obesity, cholesterol, and other cardiovascular diseases. Researchers have revealed that being overweight or obese increases a person’s risk of coronary heart disease by up to 28%. (Source: https://www.imperial.ac.uk/news/181111/fat-increased-risk-heart-disease/#:~:text=Researchers%20have%20found%20that%20being,blood%20sugar%20and%20cholesterol%20levels.)

A lack of physical activity, loss of body workout, high tobacco consumption and smoking, high-stress levels, and inadequate healthcare can also lead to an increased risk of such disease. For instance, a new brief by World Health Organizations, the World Heart Federation, and the University of Newcastle Australia revealed that every year, nearly 1.9 million die from tobacco-induced heart disease. (Source:https://www.who.int/news/item/22-09-2020-tobacco-responsible-for-20-of-deaths-from-coronary-heart-disease#:~:text=Every%20year%2C%201.9%20million%20people,Day%2C%20marked%20on%2029%20September.)

This increased prevalence of heart-related cases is pushing people to consult healthcare services leading to the market growth of heart attack diagnostics.

The skill shortage in healthcare services can restrict the growth of the market.

Healthcare organizations face numerous challenges in the recruiting process compared to other industries. The process of attracting and selecting candidates with specific clinical, medical, and administrative skills is crucial and an ongoing battle, especially for positions like physicians, nurses, and specialist practitioners.

Furthermore, the rising portion of the healthcare workforce attaining retirement age, an older population seeking more healthcare services, and new technology shifting, altogether are leading to the shortage of skilled professions along with the rise in the need and demand for the same. The situation got worse when the global pandemic hit and stretched resources to the breaking point creating immense challenges for the service providers.

The Bureau of Labor Statistics (BLS) estimates that the U.S. will face a shortage of 195,400 nurses by 2031. The various reasons from people able to live longer to the unhealthy lifestyles of people leading to the rise in chronic disease, are increasing the need for medical professionals; however, the talent supply is unable to keep up with the demand limiting the growth of the market. (Source: https://www.peoplescout.com/insights/managing-skills-shortage-health-care/)

The increasing Research & Development projects to develop digital technology for improving heart health.

The need for technology-based solutions to enhance heart health is evident yet many people are still hesitant to accept and embrace these solutions due to issues like trust concerns, relevance, and ease of use. This gap creates an opportunity for the research community to present, validate, and create scalable, and engaging health-tech solutions to pursue them. To make it possible, various healthcare service providers, key players, and the government are continuously getting involved in investments and research to find...

In 2022, the mortality rate for cardiovascular disease among men in the United States aged 35 years and older was 546.5 per 100,000 population. In comparison, the cardiovascular disease mortality rate among women was around 377 per 100,000 population. Heart disease was the leading cause of death in the United States in 2022.

2019 - 2021, county-level U.S. heart disease death rates. Dataset developed by the Centers for Disease Control and Prevention, Division for Heart Disease and Stroke Prevention.Data SourceMortality data were obtained from the National Vital Statistics System. Bridged-Race Postcensal Population Estimates were obtained from the National Center for Health Statistics. International Classification of Diseases, 10th Revision (ICD-10) codes: I00-I09, I11, I13, I20-I51; underlying cause of death.Data DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.'Data field names and descriptionsstcty_fips: state FIPS code + county FIPS codeOther fields use the following format: RRR_S_aaaa (e.g., API_M_35UP)  RRR: 3 digits represent race/ethnicity    All - Overall    AIA - American Indian and Alaska Native, non-Hispanic    ASN - Asian, non-Hispanic    BLK - Black, non-Hispanic    HIS - Hispanic NHP – Native Hawaiian or Other Pacific Islander, non-Hispanic MOR – More than one race, non-Hispanic    WHT - White, non-Hispanic  S: 1 digit represents sex    A - All    F - Female    M - Male  aaaa: 4 digits represent age. The first 2 digits are the lower bound for age and the last 2 digits are the upper bound for age. 'UP' indicates the data includes the maximum age available and 'LT' indicates ages less than the upper bound. Example: The column 'BLK_M_65UP' displays rates per 100,000 black men aged 65 years and older.MethodologyRates are calculated using a 3-year average and are age-standardized in 10-year age groups using the 2000 U.S. Standard Population. Rates are calculated and displayed per 100,000 population. Rates were spatially smoothed using a Local Empirical Bayes algorithm to stabilize risk by borrowing information from neighboring geographic areas, making estimates more statistically robust and stable for counties with small populations. Data for counties with small populations are coded as '-1' when a reliable rate could not be generated. County-level rates were generated when the following criteria were met over a 3-year time period within each of the filters (e.g., age, race, and sex).At least one of the following 3 criteria:At least 20 events occurred within the county and its adjacent neighbors.ORAt least 16 events occurred within the county.ORAt least 5,000 population years within the county.AND all 3 of the following criteria:At least 6 population years for each age group used for age adjustment if that age group had 1 or more event.The number of population years in an age group was greater than the number of events.At least 100 population years within the county.More Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods

MMWR Surveillance Summary 66 (No. SS-1):1-8 found that nonmetropolitan areas have significant numbers of potentially excess deaths from the five leading causes of death. These figures accompany this report by presenting information on potentially excess deaths in nonmetropolitan and metropolitan areas at the state level. They also add additional years of data and options for selecting different age ranges and benchmarks. Potentially excess deaths are defined in MMWR Surveillance Summary 66(No. SS-1):1-8 as deaths that exceed the numbers that would be expected if the death rates of states with the lowest rates (benchmarks) occurred across all states. They are calculated by subtracting expected deaths for specific benchmarks from observed deaths. Not all potentially excess deaths can be prevented; some areas might have characteristics that predispose them to higher rates of death. However, many potentially excess deaths might represent deaths that could be prevented through improved public health programs that support healthier behaviors and neighborhoods or better access to health care services. Mortality data for U.S. residents come from the National Vital Statistics System. Estimates based on fewer than 10 observed deaths are not shown and shaded yellow on the map. Underlying cause of death is based on the International Classification of Diseases, 10th Revision (ICD-10) Heart disease (I00-I09, I11, I13, and I20–I51) Cancer (C00–C97) Unintentional injury (V01–X59 and Y85–Y86) Chronic lower respiratory disease (J40–J47) Stroke (I60–I69) Locality (nonmetropolitan vs. metropolitan) is based on the Office of Management and Budget’s 2013 county-based classification scheme. Benchmarks are based on the three states with the lowest age and cause-specific mortality rates. Potentially excess deaths for each state are calculated by subtracting deaths at the benchmark rates (expected deaths) from observed deaths. Users can explore three benchmarks: “2010 Fixed” is a fixed benchmark based on the best performing States in 2010. “2005 Fixed” is a fixed benchmark based on the best performing States in 2005. “Floating” is based on the best performing States in each year so change from year to year. SOURCES CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES Moy E, Garcia MC, Bastian B, Rossen LM, Ingram DD, Faul M, Massetti GM, Thomas CC, Hong Y, Yoon PW, Iademarco MF. Leading Causes of Death in Nonmetropolitan and Metropolitan Areas – United States, 1999-2014. MMWR Surveillance Summary 2017; 66(No. SS-1):1-8. Garcia MC, Faul M, Massetti G, Thomas CC, Hong Y, Bauer UE, Iademarco MF. Reducing Potentially Excess Deaths from the Five Leading Causes of Death in the Rural United States. MMWR Surveillance Summary 2017; 66(No. SS-2):1–7.

Chronic diseases are defined broadly as conditions that last 1 year or more and require ongoing medical attention or limit activities of daily living or both. Chronic diseases such as heart disease, cancer, and diabetes are the leading causes of death and disability in the United States. They are also leading drivers of the nation’s $4.1 trillion in annual health care costs.

This is the latest version of US Chronic Disease 2023 with 34 columns and about 1,050,000 samples.

What is heart disease?

The term “heart disease” refers to several types of heart conditions. The most common type of heart disease in the United States is coronary artery disease (CAD), which affects the blood flow to the heart. Decreased blood flow can cause a heart attack. What are the symptoms of heart disease?

Sometimes heart disease may be “silent” and not diagnosed until a person experiences signs or symptoms of a heart attack, heart failure, or an arrhythmia. When these events happen, symptoms may include1

Heart attack: Chest pain or discomfort, upper back or neck pain, indigestion, heartburn, nausea or vomiting, extreme fatigue, upper body discomfort, dizziness, and shortness of breath.
Arrhythmia: Fluttering feelings in the chest (palpitations).
Heart failure: Shortness of breath, fatigue, or swelling of the feet, ankles, legs, abdomen, or neck veins.

Learn the Facts About Heart Disease

About 697,000 people in the United States died from heart disease in 2020—that’s 1 in every 5 deaths.1,2

Learn more facts. What are the risk factors for heart disease?

High blood pressure, high blood cholesterol, and smoking are key risk factors for heart disease. About half of people in the United States (47%) have at least one of these three risk factors.2 Several other medical conditions and lifestyle choices can also put people at a higher risk for heart disease, including

Diabetes
Overweight and obesity
Unhealthy diet
Physical inactivity
Excessive alcohol use

Learn about how heart disease and mental health disorders are related.

Learn more about heart disease, heart attack, and related conditions:

Coronary Artery Disease
Heart Attack
Men and Heart Disease
Women and Heart Disease
Other Related Conditions

What is cardiac rehabilitation?

Cardiac rehabilitation (rehab) is an important program for anyone recovering from a heart attack, heart failure, or some types of heart surgery. Cardiac rehab is a supervised program that includes

Physical activity
Education about healthy living, including healthy eating, taking medicine as prescribed, and ways to help you quit smoking
Counseling to find ways to relieve stress and improve mental health

A team of people may help you through cardiac rehab, including your health care team, exercise and nutrition specialists, physical therapists, and counselors or mental health professionals. Heart Disease Quiz

Test your knowledge of heart disease!

CDC’s Public Health Efforts Related to Heart Disease

State Public Health Actions to Prevent and Control Chronic Diseases
Million Hearts®
WISEWOMAN

More Information

American Heart Association
National Heart, Lung, and Blood Institute

References

Centers for Disease Control and Prevention, National Center for Health Statistics. About Multiple Cause of Death, 1999–2020. CDC WONDER Online Database website. Atlanta, GA: Centers for Disease Control and Prevention; 2022. Accessed February 21, 2022.
Tsao CW, Aday AW, Almarzooq ZI, Beaton AZ, Bittencourt MS, Boehme AK, et al. Heart Disease and Stroke Statistics—2022 Update: A Report From the American Heart Association. Circulation. 2022;145(8):e153–e639.
Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, et al. Heart disease and stroke statistics—2021 update: a report from the American Heart Association. Circulation. 2021;143:e254–e743.

Age-adjusted mortality rates for the contiguous United States in 2000–2005 were obtained from the Wide-ranging Online Data for Epidemiologic Research system of the U.S. Centers for Disease Control and Prevention (CDC) (2015). Age-adjusted mortality rates were weighted averages of the age-specific death rates, and they were used to account for different age structures among populations (Curtin and Klein 1995). The mortality rates for counties with < 10 deaths were suppressed by the CDC to protect privacy and to ensure data reliability; only counties with ≥ 10 deaths were included in the analyses. The underlying cause of mortality was specified using the World Health Organization’s International Statistical Classification of Diseases and Related Health Problems (10th revision; ICD-10). In this study, we focused on the all-cause mortality rate (A00-R99) and on mortality rates from the three leading causes: heart disease (I00-I09, I11, I13, and I20-I51), cancer (C00-C97), and stroke (I60- I69) (Heron 2013). We excluded mortality due to external causes for all-cause mortality, as has been done in many previous studies (e.g., Pearce et al. 2010, 2011; Zanobetti and Schwartz 2009), because external causes of mortality are less likely to be related to environmental quality. We also focused on the contiguous United States because the numbers of counties with available cause-specific mortality rates were small in Hawaii and Alaska. County-level rates were available for 3,101 of the 3,109 counties in the contiguous United States (99.7%) for all-cause mortality; for 3,067 (98.6%) counties for heart disease mortality; for 3,057 (98.3%) counties for cancer mortality; and for 2,847 (91.6%) counties for stroke mortality. The EQI includes variables representing five environmental domains: air, water, land, built, and sociodemographic (2). The domain-specific indices include both beneficial and detrimental environmental factors. The air domain includes 87 variables representing criteria and hazardous air pollutants. The water domain includes 80 variables representing overall water quality, general water contamination, recreational water quality, drinking water quality, atmospheric deposition, drought, and chemical contamination. The land domain includes 26 variables representing agriculture, pesticides, contaminants, facilities, and radon. The built domain includes 14 variables representing roads, highway/road safety, public transit behavior, business environment, and subsidized housing environment. The sociodemographic environment includes 12 variables representing socioeconomics and crime. 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: Jian, Y., L. Messer, J. Jagai, K. Rappazzo, C. Gray, S. Grabich, and D. Lobdell. Associations between environmental quality and mortality in the contiguous United States 2000-2005. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 125(3): 355-362, (2017).

Heart disease is broad term used for diseases and conditions affecting the heart and circulatory system. They are also referred as cardiovascular diseases. It is a major cause of disability all around the world. Since heart is amongst the most vital organs of the body, its diseases affect other organs and part of the body as well. There are several different types and forms of heart diseases. The most common ones cause narrowing or blockage of the coronary arteries, malfunctioning in the valves of the heart, enlargement in the size of heart and several others leading to heart failure and heart attack.

Its the heart disease dataset from this dataset we can derive various insights that help us know the weightage of each feature and how they are interrelated to each other but this time our sole aim is to detect the probability of person that will be affected by a savior heart problem or not.

Data Dictionary:

age: age in years sex: sex [1 = male, 0 = female] cp: chest pain type [Value 0: typical angina, Value 1: atypical angina, Value 2: non-anginal pain, Value 3: asymptomatic] trestbps: resting blood pressure (in mm Hg on admission to the hospital) chol: serum cholestoral in mg/dl fbs: (fasting blood sugar > 120 mg/dl) [1 = true; 0 = false] restecg: resting electrocardiographic results [Value 0: normal, Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria] thalach: maximum heart rate achieved exang: exercise induced angina [1 = yes, 0 = no] oldpeak = ST depression induced by exercise relative to rest slope: the slope of the peak exercise ST segment [Value 0: upsloping, Value 1: flat, Value 2: downsloping] ca: number of major vessels (0-3) colored by flourosopy thal: [0 = error (in the original dataset 0 maps to NaN's), 1 = fixed defect, 2 = normal, 3 = reversable defect] target (the lable): [0 = no disease, 1 = disease]

Create maps of U.S. heart disease death rates by county. Data can be stratified by age, race/ethnicity, and sex. Visit the CDC/DHDSP Atlas of Heart Disease and Stroke for additional data and maps. Atlas of Heart Disease and StrokeData SourceMortality data were obtained from the National Vital Statistics System. Bridged-Race Postcensal Population Estimates were obtained from the National Center for Health Statistics. International Classification of Diseases, 10th Revision (ICD-10) codes: I00-I09, I11, I13, I20-I51; underlying cause of death.Data DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC/DHDSP excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.' Data field names and descriptionsstcty_fips: state FIPS code + county FIPS codeOther fields use the following format: RRR_S_aaaa (e.g., API_M_35UP)   RRR: 3 digits represent race/ethnicity     All - Overall     AIA - American Indian and Alaska Native, non-Hispanic     API - Asian and Pacific Islander, non-Hispanic     BLK - Black, non-Hispanic     HIS - Hispanic     WHT - White, non-Hispanic   S: 1 digit represents sex     A - All    F - Female     M - Male  aaaa: 4 digits represent age. The first 2 digits are the lower bound for age and the last 2 digits are the upper bound for age. 'UP' indicates the data includes the maximum age available and 'LT' indicates ages less than the upper bound.  Example: The column 'BLK_M_65UP' displays rates per 100,000 black men aged 65 years and older.MethodologyRates are calculated using a 3-year average and are age-standardized in 10-year age groups using the 2000 U.S. Standard Population. Rates are calculated and displayed per 100,000 population. Rates were spatially smoothed using a Local Empirical Bayes algorithm to stabilize risk by borrowing information from neighboring geographic areas, making estimates more statistically robust and stable for counties with small populations. Data for counties with small populations are coded as '-1' when a reliable rate could not be generated. County-level rates were generated when the following criteria were met over a 3-year time period within each of the filters (e.g., age, race, and sex).At least one of the following 3 criteria: At least 20 events occurred within the county and its adjacent neighbors.ORAt least 16 events occurred within the county.ORAt least 5,000 population years within the county.AND all 3 of the following criteria:At least 6 population years for each age group used for age adjustment if that age group had 1 or more event.The number of population years in an age group was greater than the number of events.At least 100 population years within the county.More Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods

Cardiovascular disease (CVD) is one of the leading causes of death in the U.S. and is associated with a range of demographic, military, trauma, and clinical characteristics, as well as physical and mental health conditions. Older military veterans may have an increased risk of CVD due to their advanced age and military experiences. To date, however, the prevalence and health burden of CVD in population-based samples of veterans has not been well characterized. This study aimed to characterize the current prevalence of CVD and its association with sociodemographic, military, trauma, and clinical variables in a large, contemporary, and nationally representative sample of older U.S. veterans. Data were analyzed from a cross-sectional sample of 3,001 older U.S. military veterans (aged 60 and older) who participated in the National Health and Resilience in Veterans Study (NHRVS). Veterans were classified according to lifetime CVD status (CVD or no CVD, i.e., diagnoses by a healthcare professional of heart disease, heart attack, and/or stroke). To determine the association of CVD with health status, a comprehensive range of mental and physical health variables was assessed using validated self-report assessments. A total of 25.5% of veterans reported having been diagnosed with CVD. Greater age, cumulative trauma burden, nicotine use disorder, and diagnoses of hypertension, high cholesterol, and diabetes were associated with CVD. CVD was independently associated with a range of mental (odds ratios [ORs] = 1.53–2.27) and physical (ORs = 1.53–3.43) health conditions. Collectively, the results of this study suggest that one in four older U.S. veterans has report being diagnosed with CVD in their lifetimes. Given the broad range of physical and mental health conditions associated with CVD, these findings highlight the importance of integrated and multimodal prevention and intervention efforts for this population.

Heart disease and cancer remained the leading causes of death in the United States from 2018 to 2023. However, there have been slight changes in the 10 leading causes of death in the U.S. from 2018 to 2023. Most notable is that COVID-19 became the third leading cause of death in 2020 and 2021, but by 2023 it was the tenth leading cause. This statistic shows the rates of the 10 leading causes of death in the United States from 2018 to 2023.