Heart Disease Prediction Dataset

This dataset contains 1,888 records merged from five publicly available heart disease datasets. It includes 14 features that are crucial for predicting heart attack and stroke risks, covering both medical and demographic factors. Below is a detailed description of each feature.

Feature Descriptions:

1. age: Age of the patient (Numeric).
2. sex: Gender of the patient. Values: 1 = male, 0 = female.
3. cp: Chest pain type. Values: 0 = Typical angina, 1 = Atypical angina, 2 = Non-anginal pain, 3 = Asymptomatic.
4. trestbps: Resting Blood Pressure (in mm Hg) (Numeric).
5. chol: Serum Cholesterol level (in mg/dl) (Numeric).
6. fbs: Fasting blood sugar > 120 mg/dl. Values: 1 = true, 0 = false.
7. restecg: Resting electrocardiographic results. Values: 0 = Normal, 1 = ST-T wave abnormality, 2 = Left ventricular hypertrophy.
8. thalach: Maximum heart rate achieved (Numeric).
9. exang: Exercise-induced angina. Values: 1 = yes, 0 = no.
10. oldpeak: ST depression induced by exercise relative to rest (Numeric).
11. slope: Slope of the peak exercise ST segment. Values: 0 = Upsloping, 1 = Flat, 2 = Downsloping.
12. ca: Number of major vessels (0-3) colored by fluoroscopy. Values: 0, 1, 2, 3.
13. thal: Thalassemia types. Values: 1 = Normal, 2 = Fixed defect, 3 = Reversible defect.
14. target: Outcome variable (heart attack risk). Values: 1 = more chance of heart attack, 0 = less chance of heart attack.

Dataset Details:

This dataset is a combination of five publicly available heart disease datasets, with a total of 1,888 records. Merging these datasets provides a more robust foundation for training machine learning models aimed at predicting heart attack risk.

Datasets Used:

1. Heart Attack Analysis & Prediction Dataset
   Number of Records: 304
   Reference: Rahman, 2021

2. Heart Disease Dataset
   Number of Records: 1,026
   Reference: Lapp, 2019

3. Heart Attack Prediction (Dataset 3)
   Number of Records: 295
   Reference: Damarla, 2020

4. Heart Attack Prediction (Dataset 4)
   Number of Records: 271
   Reference: Anand, 2018

5. Heart CSV Dataset
   Number of Records: 290
   Reference: Nandal, 2022

Description:

This dataset includes 14 features known to contribute to heart attack risk. It is ideal for training machine learning models aimed at early detection and prevention of heart disease. The records have been cleaned by removing missing data to ensure data integrity. This dataset can be applied to various machine learning algorithms, including classification models such as Decision Trees, Neural Networks, and others.

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 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)

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.

Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worlwide. Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure.

Most cardiovascular diseases can be prevented by addressing behavioural risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity and harmful use of alcohol using population-wide strategies.

People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help.

Attribute Information:

Thirteen (13) clinical features: - age: age of the patient (years) - anaemia: decrease of red blood cells or hemoglobin (boolean) - high blood pressure: if the patient has hypertension (boolean) - creatinine phosphokinase (CPK): level of the CPK enzyme in the blood (mcg/L) - diabetes: if the patient has diabetes (boolean) - ejection fraction: percentage of blood leaving the heart at each contraction (percentage) - platelets: platelets in the blood (kiloplatelets/mL) - sex: woman or man (binary) - serum creatinine: level of serum creatinine in the blood (mg/dL) - serum sodium: level of serum sodium in the blood (mEq/L) - smoking: if the patient smokes or not (boolean) - time: follow-up period (days) - [target] death event: if the patient deceased during the follow-up period (boolean)

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The ECG Images Dataset of Cardiac Patients is an extensive collection of electrocardiogram (ECG) images designed to aid research and advancements in the field of cardiovascular medicine. This dataset provides a wealth of data that can be utilized for various analyses, including the development of diagnostic tools and the study of different cardiac conditions.

Dataset Overview

The dataset is organized into four main categories, each representing different cardiac conditions:

ECG Images of Myocardial Infarction Patients

• Number of Images: 240
• Total Dimensions: 240x12 (total of 2880 images)
• Description: These images are from patients diagnosed with myocardial infarction (MI), commonly known as a heart attack. The images reflect the ECG patterns typically associated with this critical condition.

ECG Images of Patients with Abnormal Heartbeat

• Number of Images: 233
• Total Dimensions: 233x12 (total of 2796 images)
• Description: This category includes ECG images from patients exhibiting abnormal heartbeat patterns. Such patterns may indicate a range of arrhythmias or other cardiac issues, providing crucial data for diagnostic and research purposes.

ECG Images of Patients with a History of Myocardial Infarction

• Number of Images: 172
• Total Dimensions: 172x12 (total of 2064 images)
• Description: These images come from patients who have a documented history of myocardial infarction. They offer insights into the long-term effects and recovery patterns associated with heart attacks.

Normal Person ECG Images

• Number of Images: 284
• Total Dimensions: 284x12 (total of 3408 images)
• Description: This category features ECG images from individuals with no known cardiac issues, serving as a baseline for comparison with pathological cases.

Certainly! Here’s a revised and enhanced description for your Kaggle dataset post, with the requested information removed:

ECG Images Dataset of Cardiac Patients Description The ECG Images Dataset of Cardiac Patients is an extensive collection of electrocardiogram (ECG) images designed to aid research and advancements in the field of cardiovascular medicine. This dataset provides a wealth of data that can be utilized for various analyses, including the development of diagnostic tools and the study of different cardiac conditions.

Dataset Overview The dataset is organized into four main categories, each representing different cardiac conditions:

ECG Images of Myocardial Infarction Patients

Number of Images: 240 Total Dimensions: 240x12 (total of 2880 images) Description: These images are from patients diagnosed with myocardial infarction (MI), commonly known as a heart attack. The images reflect the ECG patterns typically associated with this critical condition. ECG Images of Patients with Abnormal Heartbeat

Number of Images: 233 Total Dimensions: 233x12 (total of 2796 images) Description: This category includes ECG images from patients exhibiting abnormal heartbeat patterns. Such patterns may indicate a range of arrhythmias or other cardiac issues, providing crucial data for diagnostic and research purposes. ECG Images of Patients with a History of Myocardial Infarction

Number of Images: 172 Total Dimensions: 172x12 (total of 2064 images) Description: These images come from patients who have a documented history of myocardial infarction. They offer insights into the long-term effects and recovery patterns associated with heart attacks. Normal Person ECG Images

Number of Images: 284 Total Dimensions: 284x12 (total of 3408 images) Description: This category features ECG images from individuals with no known cardiac issues, serving as a baseline for comparison with pathological cases.

Applications

The ECG Images Dataset is a valuable resource for various applications, including:

• Machine Learning and AI Models: Train and validate models for ECG classification, anomaly detection, and predictive analytics.
• Cardiac Research: Investigate patterns and features of different cardiac conditions to improve diagnostic methods and patient outcomes.
• Diagnostic Tool Development: Create automated systems for detecting and interpreting ECG abnormalities.

Download

The dataset is available for download from Kaggle and is provided in a compressed file of approximately 194 MB.

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Context

Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Four out of 5CVD deaths are due to heart attacks and strokes, and one-third of these deaths occur prematurely in people under 70 years of age. Heart failure is a common event caused by CVDs and this dataset contains 11 features that can be used to predict a possible heart disease.

People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help.

Attribute Information

1. Age: age of the patient [years]
2. Sex: sex of the patient [M: Male, F: Female]
3. ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]
4. RestingBP: resting blood pressure [mm Hg]
5. Cholesterol: serum cholesterol [mm/dl]
6. FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]
7. 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]
8. MaxHR: maximum heart rate achieved [Numeric value between 60 and 202]
9. ExerciseAngina: exercise-induced angina [Y: Yes, N: No]
10. Oldpeak: oldpeak = ST [Numeric value measured in depression]
11. ST_Slope: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping]
12. 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/

Citation

fedesoriano. (September 2021). Heart Failure Prediction Dataset. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/heart-failure-prediction.

Acknowledgements

Creators:

1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
4. 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

Dataset

This dataset was created by Long NGzzz

Released under Database: Open Database, Contents: Database Contents

Contents

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 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 ---

The heart attack datasets were collected at Zheen hospital in Erbil, Iraq, from January 2019 to May 2019. The attributes of this dataset are: age, gender, heart rate, systolic blood pressure, diastolic blood pressure, blood sugar, ck-mb and troponin with negative or positive output. According to the provided information, the medical dataset classifies either heart attack or none. The gender column in the data is normalized: the male is set to 1 and the female to 0. The glucose column is set to 1 if it is > 120; otherwise, 0. As for the output, positive is set to 1 and negative to 0.

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 ‘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 30 September 2021.

--- 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 ---

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 Failure Prediction’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/andrewmvd/heart-failure-clinical-data on 21 November 2021.

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

About this dataset

Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worlwide. Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure.

Most cardiovascular diseases can be prevented by addressing behavioural risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity and harmful use of alcohol using population-wide strategies.

People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help.

How to use this dataset

• Create a model for predicting mortality caused by Heart Failure.
• Your kernel can be featured here!
• More datasets

Acknowledgements

If you use this dataset in your research, please credit the authors

Citation

Davide Chicco, Giuseppe Jurman: Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making 20, 16 (2020). (link)

License

CC BY 4.0

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Wallpaper by jcomp, available on Freepik.

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

Heart Failure Prediction Dataset

Downloaded from Kaggle’s fedesoriano/heart-failure-prediction.

Analysis of ‘Heart_Diseases’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/charankakaraparthi/heart-dissease on 30 September 2021.

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

Hello There , Greetings of the day !!

Here I am Providing a Small Description About the Dataset

This Dataset may be carried out in Python language to discover the accuracy based at the algorithm.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.

Thanks & Regards,

Team Head

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

Analysis of ‘Heart Failure Clinical Records Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rithikkotha/heart-failure-clinical-records-dataset on 30 September 2021.

--- No further description of dataset provided by original source ---

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

Dataset

This dataset was created by Sivagurunathan28

Contents