maiya11 / heart-disease

Heart Disease Predictor

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heart-disease

Heart Disease Predictor #Heart Disease in the United States*

  • Heart disease is the leading cause of death for men, women, and people of most racial and ethnic groups in the United States.

  • One person dies every 34 seconds in the United States from cardiovascular disease.

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

  • Heart disease cost the United States about $229 billion each year from 2017 to 2018. This includes the cost of health care services, medicines, and lost productivity due to death.

Machine learning can be used to identify a person's risk for cardiovascular disease and aid in early treatment / prevention.

*Source: cdc.gov

#Attribute Information Age: age of the patient [years]

Sex: sex of the patient [M: Male, F: Female]

ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]

RestingBP: resting blood pressure [mm Hg]

Cholesterol: serum cholesterol [mm/dl]

FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]

RestingECG: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria]

MaxHR: maximum heart rate achieved [Numeric value between 60 and 202]

ExerciseAngina: exercise-induced angina [Y: Yes, N: No]

Oldpeak: oldpeak = ST [Numeric value measured in depression]

ST_Slope: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping]

HeartDisease: output class [1: Heart Disease, 0: Normal]

#Source

This dataset was created by combining different datasets already available independently, but not combined before. In this dataset, 5 heart datasets are combined over 11 common features which makes it the largest heart disease dataset available so far for research purposes. The five datasets used for its curation are:

Cleveland: 303 observations

Hungarian: 294 observations

Switzerland: 123 observations

Long Beach VA: 200 observations

Stalog (Heart) Data Set: 270 observations

Total: 1190 observations

Duplicated: 272 observations

Final dataset: 918 observations

Every dataset used can be found under the Index of heart disease datasets from UCI Machine Learning Repository on the following link: https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/

#Creators:

Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.

University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.

University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.

V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.

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Heart Disease Predictor


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