This heart disease dataset is curated by combining 5 popular heart disease datasets already available independently but not combined before. In this dataset

This heart disease dataset is acquired from one o f the multispecialty hospitals in India. Over 14 common features which makes it one of the heart disease dataset available so far for research purposes. This dataset consists of 1000 subjects with 12 features. This dataset will be useful for building a early-stage heart disease detection as well as to generate predictive machine learning models.

This heart disease dataset is curated by combining 3 popular heart disease datasets. The first dataset (Collected from Kaggle) contains 70000 records with 11 independent features which makes it the largest heart disease dataset available so far for research purposes. These data were collected at the moment of medical examination and information given by the patient. Second and third datasets contain 303 and 293 intstances respectively with 13 common features. The three datasets used for its curation are:Cardio Data (Kaggle Dataset)

Dataset

This dataset was created by SIRMEOW123

Contents

Analysis of ‘Heart Disease Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yasserh/heart-disease-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Description:

This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to this date. The "goal" field refers to the presence of heart disease in the patient. It is integer-valued from 0 (no presence) to 4.

Acknowledgements:

This dataset has been referred from Kaggle.

Objective:

• Understand the Dataset & cleanup (if required).
• Build classification models to predict whether or not the patients have Heart Disease.
• Also fine-tune the hyperparameters & compare the evaluation metrics of various classification algorithms.

--- Original source retains full ownership of the source dataset ---

Adaptation of http://archive.ics.uci.edu/ml/datasets/Heart+Disease

Ready for usage with ehrapy

About Dataset Context: The leading cause of death in the developed world is heart disease. Therefore there needs to be work done to help prevent the risks of of having a heart attack or stroke.

Content: Use this dataset to predict which patients are most likely to suffer from a heart disease in the near future using the features given.

Acknowledgement: This data comes from the University of California Irvine's Machine Learning Repository at https://archive.ics.uci.edu/ml/datasets/Heart+Disease.

Analysis of ‘Heart Disease Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/lykin22/heart-disease-dataset on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Overview

The data science lifecycle is designed for big data issues and data science projects. Generally, the data science project consists of seven steps which are problem definition, data collection, data preparation, data exploration, data modelling and model evaluation. In this project, I will go through these steps in order to build a heart disease classifier. To build the heart disease classifier by using UCI heart disease) dataset.

Description:

This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. The "goal" field refers to the presence of heart disease in the patient. It is integer-valued from 0 (no presence) to 4. Experiments with the Cleveland database have concentrated on simply attempting to distinguish presence (values 1,2,3,4) from absence (value 0). The names and social security numbers of the patients were recently removed from the database, replaced with dummy values. One file has been "processed", that one containing the Cleveland database. All four unprocessed files also exist in this directory. To see Test Costs (donated by Peter Turney), please see the folder "Costs"

Dataset

The dataset has 14 attributes: 1. age: age in years 2. sex: sex (1 = male; 0 = female) 3. cp: chest pain type (Value 0: typical angina; Value 1: atypical angina; Value 2: non-anginal pain; Value 3: asymptomatic) 4. trestbps: resting blood pressure in mm Hg on admission to the hospital 5. chol: serum cholestoral in mg/dl 6. fbs: fasting blood sugar > 120 mg/dl (1 = true; 0 = false) 7. restecg: resting electrocardiographic results (Value 0: normal; Value 1: having ST-T wave abnormality; Value 2: probable or definite left ventricular hypertrophy) 8. thalach: maximum heart rate achieved 9. exang: exercise induced angina (1 = yes; 0 = no) 10. oldpeak: ST depression induced by exercise relative to rest 11. slope: the slope of the peak exercise ST segment (Value 0: upsloping; Value 1: flat; Value 2: downsloping) 12. ca: number of major vessels (0-3) colored by flourosopy 13. thal: thalassemia (3 = normal; 6 = fixed defect; 7 = reversable defect) 14. target: heart disease (1 = no, 2 = yes)

If you find this dataset useful, please consider upvoting ❤️

--- Original source retains full ownership of the source dataset ---

This data set dates from 1988 and consists of four databases: Cleveland, Hungary, Switzerland, and Long Beach V. It contains 9 attributes and is a shorter version of the original model. The "target" field refers to the presence of heart disease in the patient. It is integer valued 0 = no disease and 1 = disease. Source of the original data can be found here: https://archive.ics.uci.edu/ml/datasets/heart+Disease

1. age
2. sex
3. chest pain type (4 values)
4. resting blood pressure
5. serum cholestoral in mg/dl
6. fasting blood sugar > 120 mg/dl
7. heart rate max- maximum heart rate achieved
8. angina - exercise induced angina 0 no, 1 yes
9. target - 1 = heart disease, 0 = no heart disease

Analysis of ‘Cardiovascular diseases dataset (clean)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/aiaiaidavid/cardio-data-dv13032020 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Description of the data set

This data set is a cleaned up copy of cardio_train.csv which can be found at:

https://www.kaggle.com/sulianova/cardiovascular-disease-dataset

The original data set has been analyzed with Excel, correcting negative values, and removing outliers.

A number of features in the dataset are used to predict the presence or absence of a cardiovascular disease.

Below is a description of the features:

AGE: integer (years of age)
HEIGHT: integer (cm) 
WEIGHT: integer (kg)
GENDER: categorical (1: female, 2: male)
AP_HIGH: systolic blood pressure, integer
AP_LOW: diastolic blood pressure, integer 
CHOLESTEROL: categorical (1: normal, 2: above normal, 3: well above normal)
GLUCOSE: categorical (1: normal, 2: above normal, 3: well above normal)
SMOKE: categorical (0: no, 1: yes)
ALCOHOL: categorical (0: no, 1: yes)
PHYSICAL_ACTIVITY: categorical (0: no, 1: yes)

And the target variable:

CARDIO_DISEASE: categorical (0: no, 1: yes)

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Heart Disease Prediction using DifferentTechniques’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jillanisofttech/heart-disease-prediction-using-differenttechniques on 13 November 2021.

--- Dataset description provided by original source is as follows ---

Context: The leading cause of death in the developed world is heart disease. Therefore there needs to be work done to help prevent the risks of having a heart attack or stroke.

Content: Use this dataset to predict which patients are most likely to suffer from heart disease in the near future using the features given.

Acknowledgment: This data comes from the UCI at https://archive.ics.uci.edu/ml/datasets/Heart+Disease.

--- Original source retains full ownership of the source dataset ---

Our research demonstrates that machine learning algorithms can effectively predict heart failure, highlighting high-accuracy models that improve detection and treatment. The Kaggle “Heart Failure” dataset, with 918 instances and 12 key features, was preprocessed to remove outliers and features a distribution of cases with and without heart disease (508 and 410). Five models were evaluated: the random forest achieved the highest accuracy (92%) and was consolidated as the most effective at classifying cases. Logistic regression and multilayer perceptron were also quite accurate (89%), while decision tree and k-nearest neighbors performed less well, showing that k-neighbors is less suitable for this data. F1 scores confirmed the random forest as the optimal one, benefiting from preprocessing and hyperparameter tuning. The data analysis revealed that age, blood pressure and cholesterol correlate with disease risk, suggesting that these models may help prioritize patients at risk and improve their preventive management. The research underscores the potential of these models in clinical practice to improve diagnostic accuracy and reduce costs, supporting informed medical decisions and improving health outcomes.

Analysis of ‘Heart Disease Mortality Dataset ❤️’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arjunbhaybhang/heart-disease-mortality-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

DESCRIPTION Heart Disease Mortality Data Among US Adults (35+) by State/Territory and County – 2016-2018

Don't forget to upvote 👍

SUMMARY Original Title: Heart Disease Mortality Data Among US Adults (35+) by State/Territory and County – 2016-2018

2016 to 2018, 3-year average. Rates are age-standardized. County rates are spatially smoothed. The data can be viewed by gender and race/ethnicity. Data source: National Vital Statistics System. Additional data, maps, and methodology can be viewed on the Interactive Atlas of Heart Disease and Stroke http://www.cdc.gov/dhdsp/maps/atlas

Source: https://catalog.data.gov/dataset/heart-disease-mortality-data-among-us-adults-35-by-state-territory-and-county-2016-2018-c0d58 Last updated at https://catalog.data.gov/organization/hhs-gov : 2021-04-21

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Heart Disease Cleveland UCI’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/cherngs/heart-disease-cleveland-uci on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The data is already presented in https://www.kaggle.com/ronitf/heart-disease-uci but there are some descriptions and values that are wrong as discussed in https://www.kaggle.com/ronitf/heart-disease-uci/discussion/105877. So, here is re-processed dataset that was cross-checked with the original data https://archive.ics.uci.edu/ml/datasets/Heart+Disease.

Content

There are 13 attributes 1. age: age in years 2. sex: sex (1 = male; 0 = female) 3. cp: chest pain type -- Value 0: typical angina -- Value 1: atypical angina -- Value 2: non-anginal pain -- Value 3: asymptomatic 4. trestbps: resting blood pressure (in mm Hg on admission to the hospital) 5. chol: serum cholestoral in mg/dl 6. fbs: (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) 7. 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 8. thalach: maximum heart rate achieved 9. exang: exercise induced angina (1 = yes; 0 = no) 10. oldpeak = ST depression induced by exercise relative to rest 11. slope: the slope of the peak exercise ST segment -- Value 0: upsloping -- Value 1: flat -- Value 2: downsloping 12. ca: number of major vessels (0-3) colored by flourosopy 13. thal: 0 = normal; 1 = fixed defect; 2 = reversable defect and the label 14. condition: 0 = no disease, 1 = disease

Acknowledgements

Data posted on Kaggle: https://www.kaggle.com/ronitf/heart-disease-uci Description of the data above: https://www.kaggle.com/ronitf/heart-disease-uci/discussion/105877 Original data https://archive.ics.uci.edu/ml/datasets/Heart+Disease

Creators: Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. University Hospital, Zurich, Switzerland: William Steinbr 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. Donor: David W. Aha (aha '@' ics.uci.edu) (714) 856-8779

Inspiration

With the attributes described above, can you predict if a patient has heart disease?

--- Original source retains full ownership of the source dataset ---

Welcome to the Cardiovascular Health Dataset, a comprehensive collection of data encompassing various parameters related to heart health. This dataset is a valuable resource for researchers, healthcare professionals, and data enthusiasts seeking insights into the factors influencing heart disease.

Analysis of ‘Heart Disease Classification Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sumaiyatasmeem/heart-disease-classification-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Use this heart disease classification dataset to predict which patients are most likely to suffer from a heart disease in the near future using the features given.

Content

Data Dictionary

age: Displays the age of the individual.

sex: Displays the gender of the individual using the following format : 1 = male 0 = female

cp- Chest-pain type: displays the type of chest-pain experienced by the individual using the following format : 0 = typical angina 1 = atypical angina 2 = non — anginal pain 3 = asymptotic

trestbps- Resting Blood Pressure: displays the resting blood pressure value of an individual in mmHg (unit). anything above 130-140 is typically cause for concern.

chol- Serum Cholestrol: displays the serum cholesterol in mg/dl (unit)

fbs- Fasting Blood Sugar: compares the fasting blood sugar value of an individual with 120mg/dl. If fasting blood sugar > 120mg/dl then : 1 (true) else : 0 (false) '>126' mg/dL signals diabetes

restecg- Resting ECG : displays resting electrocardiographic results 0 = normal 1 = having ST-T wave abnormality 2 = left ventricular hyperthrophy

thalach- Max heart rate achieved : displays the max heart rate achieved by an individual.

exang- Exercise induced angina : 1 = yes 0 = no

oldpeak- ST depression induced by exercise relative to rest: displays the value which is an integer or float.

slope- Slope of the peak exercise ST segment : 0 = upsloping: better heart rate with excercise (uncommon) 1 = flat: minimal change (typical healthy heart) 2 = downsloping: signs of unhealthy heart

ca- Number of major vessels (0–3) colored by flourosopy : displays the value as integer or float.

thal : Displays the thalassemia : 1,3 = normal 6 = fixed defect 7 = reversible defect: no proper blood movement when excercising

target : Displays whether the individual is suffering from heart disease or not : 1 = yes 0 = no

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

--- Original source retains full ownership of the source dataset ---

ECG images dataset of Cardiac Patients created under the auspices of Ch. Pervaiz Elahi Institute of Cardiology Multan, Pakistan that aims to help the scientific community for conducting the research for Cardiovascular diseases.

Analysis of ‘Heart Attack Analysis & Prediction Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rashikrahmanpritom/heart-attack-analysis-prediction-dataset on 13 February 2022.

--- Dataset description provided by original source is as follows ---

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About this dataset

• Age : Age of the patient

• Sex : Sex of the patient

• exang: exercise induced angina (1 = yes; 0 = no)

• ca: number of major vessels (0-3)

• cp : Chest Pain type chest pain type

  • Value 1: typical angina
  • Value 2: atypical angina
  • Value 3: non-anginal pain
  • Value 4: asymptomatic

• trtbps : resting blood pressure (in mm Hg)

• chol : cholestoral in mg/dl fetched via BMI sensor

• fbs : (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)

• rest_ecg : 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

• target : 0= less chance of heart attack 1= more chance of heart attack

n

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by ketan gangal

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Muhammad Arslan Q

Released under Apache 2.0

Contents