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Dataset . 2025
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ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
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ZENODO
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Dataset . 2025
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Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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BRFSS 2020 Heart Disease Dataset(Cleaned Version)

Authors: Koushal Kumar; BP Pande;

BRFSS 2020 Heart Disease Dataset(Cleaned Version)

Abstract

Originally, the dataset come from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to gather data on the health status of U.S. residents. As the CDC describes: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as 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 (as of February 15, 2022) includes data from 2020. It consists of 401,958 rows and 279 columns. The vast majority of columns are questions asked to respondents about their health status, such as "Do you have serious difficulty walking or climbing stairs?" or "Have you smoked at least 100 cigarettes in your entire life? [Note: 5 packs = 100 cigarettes]". To improve the efficiency and relevance of our analysis, we removed certain attributes from the original BRFSS dataset. Many of the 279 original attributes included administrative codes, metadata, or survey-specific variables that do not contribute meaningfully to heart disease prediction—such as respondent IDs, timestamps, state-level identifiers, and detailed lifestyle questions unrelated to cardiovascular health. By focusing on a carefully selected subset of 18 attributes directly linked to medical, behavioral, and demographic factors known to influence heart health, we streamlined the dataset. This not only reduced computational complexity but also improved model interpretability and performance by eliminating noise and irrelevant information. All predicting variables could be divided into 4 broad categories: Demographic factors: sex, age category (14 levels), race, BMI (Body Mass Index) Diseases: weather respondent ever had such diseases as asthma, skin cancer, diabetes, stroke or kidney disease (not including kidney stones, bladder infection or incontinence) Unhealthy habits: Smoking - respondents that smoked at least 100 cigarettes in their entire life (5 packs = 100 cigarettes) Alcohol Drinking - heavy drinkers (adult men having more than 14 drinks per week and adult women having more than 7 drinks per week General Health: Difficulty Walking - weather respondent have serious difficulty walking or climbing stairs Physical Activity - adults who reported doing physical activity or exercise during the past 30 days other than their regular job Sleep Time - respondent’s reported average hours of sleep in a 24-hour period Physical Health - number of days being physically ill or injured (0-30 days) Mental Health - number of days having bad mental health (0-30 days) General Health - respondents declared their health as ’Excellent’, ’Very good’, ’Good’ ,’Fair’ or ’Poor’ Below is a description of the features collected for each patient: S. No. Original Variable/Attribute Coded Variable/Attribute Interpretation 1. CVDINFR4 HeartDisease Those who have ever had CHD or myocardial infarction 2. _BMI5CAT BMI Body Mass Index 3. _SMOKER3 Smoking Have you ever smoked more than 100 cigarettes in your life? (The answer is either yes or no) 4. _RFDRHV7 AlcoholDrinking Adult men who drink more than 14 drinks per week and adult women who consume more than 7 drinks per week are considered heavy drinkers 5. CVDSTRK3 Stroke (Ever told) (you had) a stroke? 6. PHYSHLTH PhysicalHealth It includes physical illness and injury during the past 30 days 7. MENTHLTH MentalHealth How many days in the last 30 days have you had poor mental health? 8. DIFFWALK DiffWalking Are you having trouble walking or climbing stairs? 9. SEXVAR Sex Are you male or female? 10. _AGE_G AgeCategory Out of given fourteen age groups, which group do you fall into? 11. _IMPRACE Race Imputed race/ethnicity value 12. DIABETE4 Diabetic (Ever told) (you had) diabetes? 13. EXERANY2 PhysicalActivity Adults who reported engaging in physical activity or exercise from outside their regular work in the previous 30 days 14. GENHLTH GenHealth How would you rate your overall health? 15. SLEPTIM1 SleepTime During 24 hours, on average, how many hours of sleep do you get? 16. CHASTHMA Asthma (Ever told) (you had) asthma? 17. CHCKDNY2 KidneyDisease Were you ever told you had kidney disease, other than stones, bladder infection, or incontinence? 18. CHCSCNCR SkinCancer Ever told (you had) skin cancer?

https://www.cdc.gov/brfss/annual_data/2020/pdf/codebook20_llcp-v2-508.pdf

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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