Dataset

This dataset was created by Muneer Iqbal24

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Priyanka

Contents

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)

Heart Disease Data combined from UCI repository of following places:

Cleveland, Hungary, Switzerland, and VA Long Beach

Features: Age: Age of individual. 20-80 Sex: This is the gender of the individual. It is represented as a binary value where 1 stands for male and 0 stands for female. ChestPainType: This categorizes the type of chest pain experienced by the individual. The values are: Value 1: Typical angina, which is chest pain related to the heart. Value 2: Atypical angina, which is chest pain not related to the heart. Value 3: Non-anginal pain, which is typically sharp and non-continuous. Value 4: Asymptomatic, meaning the individual experiences no symptoms. RestingBP: This is the individual’s resting blood pressure (in mm Hg) when they are at rest. Cholesterol: This is the individual’s cholesterol level, measured in mg/dl. FastingBS: This indicates whether the individual’s fasting blood sugar is greater than 120 mg/dl. It is represented as a binary value where 1 stands for true and 0 stands for false. MaxHR: This is the maximum heart rate achieved by the individual. ExerciseAngina: This indicates whether the individual experiences angina (chest pain) induced by exercise. It is represented as a binary value where 1 stands for yes and 0 stands for no.

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

Ready for usage with ehrapy

Heart

The Heart dataset from the UCI ML repository. Does the patient have heart disease?

  Configurations and tasks

Configuration Task

hungary Binary classification

  Usage

from datasets import load_dataset

dataset = load_dataset("mstz/heart", "hungary")["train"]

This dataset contains cleaned clinical, demographic, and physiological attributes collected from patients undergoing medical evaluation for potential heart disease. It is widely used for predictive modeling in healthcare, particularly to identify whether a patient is likely to have heart disease based on diagnostic measurements.

The target variable (num) indicates the presence or absence of heart disease, making this dataset suitable for binary classification tasks.

Dataset Structure

Columns (features):

1. age → Age of the patient (years)

2. sex → Gender (Male / Female)

3. cp → Chest pain type

• typical angina

• atypical angina

• non-anginal pain

• asymptomatic

1. trestbps → Resting blood pressure (mm Hg)

2. chol → Serum cholesterol (mg/dl)

3. fbs → Fasting blood sugar (True if > 120 mg/dl, else False)

4. restecg → Resting electrocardiographic results (normal, lv hypertrophy, etc.)

5. thalch → Maximum heart rate achieved

6. exang → Exercise induced angina (True = yes, False = no)

7. oldpeak → ST depression induced by exercise relative to rest (numeric value)

8. num → Target variable (Heart disease diagnosis)

        0 → No heart disease
        1-4 → Heart disease present (severity levels)
   

Use Cases-

• Predictive modeling for heart disease classification

• Exploratory data analysis (EDA) of risk factors

• Machine learning projects in healthcare analytics

• Medical research on correlations between risk factors and heart disease

❤️ Heart Disease Dataset (Enhanced with Feature Engineering)

  📌 Overview

This dataset is an enhanced version of the classic UCI Heart Disease dataset, enriched with extensive feature engineering to support advanced data analysis and machine learning applications. In addition to the original clinical features, several derived variables have been introduced to provide deeper insights into cardiovascular risk patterns. These engineered features allow for improved predictive… See the full description on the dataset page: https://huggingface.co/datasets/nezahatkorkmaz/heart-disease-dataset.

Original Data from: https://archive.ics.uci.edu/ml/datasets/Heart+Disease Changes made: - four rows with missing values were removed, leaving 299 records - Chest Pain Type, Restecg, Thal variables were converted to indicator variables - class attribute binarised to -1 (no disease) / +1 disease (original values 1,2,3) Attributes: Col 0: CLASS: -1: no disease +1: disease Col 1: Age (cts) Col 2: Sex (0/1) Col 3: indicator (0/1) for typ angina Col 4: indicator for atyp angina Col 5: indicator for non-ang pain Col 6: resting blood pressure (cts) Col 7: Serum cholest (cts) Col 8: fasting blood sugar >120mg/dl (0/1) Col 9: indicator for electrocardio value 1 Col 10: indicator for electrocardio value 2 Col 11: Max heart rate (cts) Col 12: exercised induced angina (0/1) Col 13: ST depression induced by exercise (cts) Col 14: indicator for slope of peak exercise up Col 15: indicator for slope of peak exercise down Col 16: no major vessels colored by fluro (ctsish: 0,1,2,3) Col 17: Thal reversible defect indicator Col 18: Thal fixed defect indicator Col 19: Class 0-4, where 0 is disease not present, 1-4 is present

Dataset

This dataset was created by Nishant Bansal

Contents

Heart Disease Data: Enhanced with Feature Engineering for Advanced Analysis

This dataset is an advanced version of the classic UCI Machine Learning heart disease dataset, enriched with feature engineering to support more sophisticated analyses. The original features have been supplemented with newly derived attributes that help to better understand and model cardiovascular risk factors.

Dataset Description:

1. age: Age of the patient (int).
2. sex: Gender of the patient; 1 for male, 0 for female (int).
3. cp: Chest pain type (int); 1: Typical angina, 2: Atypical angina, 3: Non-anginal pain, 4: Asymptomatic.
4. trestbps: Resting blood pressure (mm Hg) (int).
5. chol: Serum cholesterol level (mg/dl) (int).
6. fbs: Fasting blood sugar > 120 mg/dl; 1 if true, 0 if false (int).
7. restecg: Resting electrocardiographic results (int); 0: Normal, 1: ST-T wave abnormality, 2: Showing probable or definite left ventricular hypertrophy.
8. thalach: Maximum heart rate achieved (int).
9. exang: Exercise induced angina; 1 if yes, 0 if no (int).
10. oldpeak: Depression induced by exercise relative to rest (float).
11. slope: Slope of the peak exercise ST segment (int); 1: Upsloping, 2: Flat, 3: Downsloping.
12. ca: Number of major vessels colored by fluoroscopy (float).
13. thal: Thalassemia; 3: Normal, 6: Fixed defect, 7: Reversible defect (float).
14. num: Heart disease diagnosis (int); 0: No disease, 1-4: Disease present with increasing severity.

Derived Features:

1. age_group: Age category; '30s', '40s', '50s', '60s'.
2. cholesterol_level: Cholesterol level category; 'low', 'normal', 'high'.
3. bp_level: Blood pressure category; 'low', 'normal', 'high'.
4. risk_score: Calculated risk score using the formula: (\text{age} \times \text{chol} / 1000 + \text{trestbps} / 100).
5. symptom_severity: Severity of symptoms; calculated as (\text{cp} \times \text{oldpeak}).
6. log_chol: Logarithm of cholesterol level.
7. log_trestbps: Logarithm of resting blood pressure.
8. age_squared: Square of age.
9. chol_squared: Square of cholesterol level.
10. age_thalach_ratio: Ratio of maximum heart rate to age (plus 1 to avoid division by zero).
11. risk_factor: Risk factor calculated as (\text{cp} \times \text{oldpeak} \times \text{thal}).
12. missing_values: Count of missing values in 'ca' and 'thal'.
13. chol_trestbps_ratio: Ratio of cholesterol level to resting blood pressure.
14. log_thalach_chol: Logarithm of the product of maximum heart rate and cholesterol level.
15. symptom_zscore: Z-score of symptom severity.
16. avg_chol_by_age_group: Average cholesterol level for the age group.
17. thalach_chol_diff: Difference between maximum heart rate and cholesterol level.
18. symptom_severity_diff: Difference in symptom severity compared to the average for the age group.
19. age_chol_effect: Product of age and cholesterol level.
20. thalach_risk_effect: Product of maximum heart rate and risk score.
21. age_trestbps_effect: Product of age and resting blood pressure.
22. chol_risk_ratio: Ratio of cholesterol level to risk score.

These additional features facilitate a deeper analysis of cardiovascular health by incorporating various derived metrics and categorizations, enhancing the overall utility of the dataset for predictive modeling and data exploration.

The UCI Heart Disease Dataset is a heart disease dataset that contains a total of 76 attributes, but all published experiments refer to a subset of 14 attributes, of which the Cleveland database is the only one ML researchers have used.goal ” field refers to whether a patient has heart disease or not, and the experiments on the Cleveland database focused on trying to distinguish between presence (values 1, 2, 3, 4) and absence (value 0).

Dataset

This dataset was created by Ronak Kantariya

Released under CC0: Public Domain

Contents

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

Author: H. Altay Guvenir, Burak Acar, Haldun Muderrisoglu
Source: UCI
Please cite: UCI

Cardiac Arrhythmia Database
The aim is to determine the type of arrhythmia from the ECG recordings. This database contains 279 attributes, 206 of which are linear valued and the rest are nominal.

Concerning the study of H. Altay Guvenir: "The aim is to distinguish between the presence and absence of cardiac arrhythmia and to classify it in one of the 16 groups. Class 01 refers to 'normal' ECG classes, 02 to 15 refers to different classes of arrhythmia and class 16 refers to the rest of unclassified ones. For the time being, there exists a computer program that makes such a classification. However, there are differences between the cardiologist's and the program's classification. Taking the cardiologist's as a gold standard we aim to minimize this difference by means of machine learning tools.

The names and id numbers of the patients were recently removed from the database.

Attribute Information

  1 Age: Age in years , linear
  2 Sex: Sex (0 = male; 1 = female) , nominal
  3 Height: Height in centimeters , linear
  4 Weight: Weight in kilograms , linear
  5 QRS duration: Average of QRS duration in msec., linear
  6 P-R interval: Average duration between onset of P and Q waves
   in msec., linear
  7 Q-T interval: Average duration between onset of Q and offset
   of T waves in msec., linear
  8 T interval: Average duration of T wave in msec., linear
  9 P interval: Average duration of P wave in msec., linear
 Vector angles in degrees on front plane of:, linear
 10 QRS
 11 T
 12 P
 13 QRST
 14 J
 15 Heart rate: Number of heart beats per minute ,linear
 Of channel DI:
  Average width, in msec., of: linear
  16 Q wave
  17 R wave
  18 S wave
  19 R' wave, small peak just after R
  20 S' wave
  21 Number of intrinsic deflections, linear
  22 Existence of ragged R wave, nominal
  23 Existence of diphasic derivation of R wave, nominal
  24 Existence of ragged P wave, nominal
  25 Existence of diphasic derivation of P wave, nominal
  26 Existence of ragged T wave, nominal
  27 Existence of diphasic derivation of T wave, nominal
 Of channel DII: 
  28 .. 39 (similar to 16 .. 27 of channel DI)
 Of channels DIII:
  40 .. 51
 Of channel AVR:
  52 .. 63
 Of channel AVL:
  64 .. 75
 Of channel AVF:
  76 .. 87
 Of channel V1:
  88 .. 99
 Of channel V2:
  100 .. 111
 Of channel V3:
  112 .. 123
 Of channel V4:
  124 .. 135
 Of channel V5:
  136 .. 147
 Of channel V6:
  148 .. 159
 Of channel DI:
  Amplitude , * 0.1 milivolt, of
  160 JJ wave, linear
  161 Q wave, linear
  162 R wave, linear
  163 S wave, linear
  164 R' wave, linear
  165 S' wave, linear
  166 P wave, linear
  167 T wave, linear
  168 QRSA , Sum of areas of all segments divided by 10,
    ( Area= width * height / 2 ), linear
  169 QRSTA = QRSA + 0.5 * width of T wave * 0.1 * height of T
    wave. (If T is diphasic then the bigger segment is
    considered), linear
 Of channel DII:
  170 .. 179
 Of channel DIII:
  180 .. 189
 Of channel AVR:
  190 .. 199
 Of channel AVL:
  200 .. 209
 Of channel AVF:
  210 .. 219
 Of channel V1:
  220 .. 229
 Of channel V2:
  230 .. 239
 Of channel V3:
  240 .. 249
 Of channel V4:
  250 .. 259
 Of channel V5:
  260 .. 269
 Of channel V6:
  270 .. 279

Class code - class - number of instances:

  01       Normal        245
  02       Ischemic changes (Coronary Artery Disease)  44
  03       Old Anterior Myocardial Infarction      15
  04       Old Inferior Myocardial Infarction      15
  05       Sinus tachycardy    13
  06       Sinus bradycardy    25
  07       Ventricular Premature Contraction (PVC)    3
  08       Supraventricular Premature Contraction    2
  09       Left bundle branch block     9 
  10       Right bundle branch block    50
  11       1. degree AtrioVentricular block    0 
  12       2. degree AV block        0
  13       3. degree AV block        0
  14       Left ventricule hypertrophy        4
  15       Atrial Fibrillation or Flutter        5
  16       Others         22

Heart Disease Risk Prediction Dataset

Overview

This synthetic dataset is designed to predict the risk of heart disease based on a combination of symptoms, lifestyle factors, and medical history. Each row in the dataset represents a patient, with binary (Yes/No) indicators for symptoms and risk factors, along with a computed risk label indicating whether the patient is at high or low risk of developing heart disease.

The dataset contains 70,000 samples, making it suitable for training machine learning models for classification tasks. The goal is to provide researchers, data scientists, and healthcare professionals with a clean and structured dataset to explore predictive modeling for cardiovascular health.

This dataset is a side project of EarlyMed, developed by students of Vellore Institute of Technology (VIT-AP). EarlyMed aims to leverage data science and machine learning for early detection and prevention of chronic diseases.

Dataset Features

Input Features

Symptoms (Binary - Yes/No)

1. Chest Pain (chest_pain): Presence of chest pain, a common symptom of heart disease.
2. Shortness of Breath (shortness_of_breath): Difficulty breathing, often associated with heart conditions.
3. Unexplained Fatigue (fatigue): Persistent tiredness without an obvious cause.
4. Palpitations (palpitations): Irregular or rapid heartbeat.
5. Dizziness/Fainting (dizziness): Episodes of lightheadedness or fainting.
6. Swelling in Legs/Ankles (swelling): Swelling due to fluid retention, often linked to heart failure.
7. Pain in Arm/Jaw/Neck/Back (radiating_pain): Radiating pain, a hallmark of angina or heart attacks.
8. Cold Sweats & Nausea (cold_sweats): Symptoms commonly associated with acute cardiac events.

Risk Factors (Binary - Yes/No or Continuous)

1. Age (age): Patient's age in years (continuous variable).
2. High Blood Pressure (hypertension): History of hypertension (Yes/No).
3. High Cholesterol (cholesterol_high): Elevated cholesterol levels (Yes/No).
4. Diabetes (diabetes): Diagnosis of diabetes (Yes/No).
5. Smoking History (smoker): Whether the patient is a smoker (Yes/No).
6. Obesity (obesity): Obesity status (Yes/No).
7. Family History of Heart Disease (family_history): Family history of cardiovascular conditions (Yes/No).

Output Label

• Heart Disease Risk (risk_label): Binary label indicating the risk of heart disease:
  • 0: Low risk
  • 1: High risk

Data Generation Process

This dataset was synthetically generated using Python libraries such as numpy and pandas. The generation process ensured a balanced distribution of high-risk and low-risk cases while maintaining realistic correlations between features. For example: - Patients with multiple risk factors (e.g., smoking, hypertension, and diabetes) were more likely to be labeled as high risk. - Symptom patterns were modeled after clinical guidelines and research studies on heart disease.

Sources of Inspiration

The design of this dataset was inspired by the following resources:

Books

• "Harrison's Principles of Internal Medicine" by J. Larry Jameson et al.: A comprehensive resource on cardiovascular diseases and their symptoms.
• "Mayo Clinic Cardiology" by Joseph G. Murphy et al.: Provides insights into heart disease risk factors and diagnostic criteria.

Research Papers

• Framingham Heart Study: A landmark study identifying key risk factors for cardiovascular disease.
• American Heart Association (AHA) Guidelines: Recommendations for diagnosing and managing heart disease.

Existing Datasets

• UCI Heart Disease Dataset: A widely used dataset for heart disease prediction.
• Kaggle’s Heart Disease datasets: Various datasets contributed by the community.

Clinical Guidelines

• Centers for Disease Control and Prevention (CDC): Information on heart disease symptoms and risk factors.
• World Health Organization (WHO): Global statistics and risk factor analysis for cardiovascular diseases.

Applications

This dataset can be used for a variety of purposes:

1. Machine Learning Research:

   • Train classification models (e.g., Logistic Regression, Random Forest, XGBoost) to predict heart disease risk.
   • Experiment with feature engineering, model tuning, and evaluation metrics like Accuracy, Precision, Recall, and ROC-AUC.

2. Healthcare Analytics:

   • Identify key risk factors contributing to heart disease.
   • Develop decision support systems for early detection of cardiovascular risks.

3. Educational Purposes:

   • Teach students and practitioners about predictive modeling in healthcare.
   • Demonstrate the importance of feature selection...

Heart Disease Dataset from UCI Repository

1. age - age in years
2. sex (1 = male; 0 = female)
3. cp - chest pain type
   • 0: Typical angina: chest pain related decrease blood supply to the heart
   • 1: Atypical angina: chest pain not related to heart
   • 2: Non-anginal pain: typically esophageal spasms (non heart related)
   • 3: Asymptomatic: chest pain not showing signs of disease
4. trestbps - resting blood pressure (in mm Hg on admission to the hospital) anything above 130-140 is typically cause for concern
5. chol - serum cholostoral in mg/dl
   • serum = LDL + HDL + .2 * triglycerides
   • above 200 is cause for concern
6. fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
   • '>126' mg/dL signals diabetes
7. restecg - resting electrocardiographic results
   • 0: Nothing to note
   • 1: ST-T Wave abnormality
     • can range from mild symptoms to severe problems
     • signals non-normal heart beat
   • 2: Possible or definite left ventricular hypertophy
     • Enlarged heart's main pumping chamber
8. thalach - maximum heart rate achieved
9. exange - exercise induced angina (1 = yes; 0 = no)
10. oldpeak - ST depression induced by exercise relative to rest looks at stress of heart during exercise unhealthy heart will stress more
11. slope - the slope of the peak exercise ST segment
    • 0: Upsloping: better heart rate with exercise (uncommon)
    • 1: Flatsloping: minimal change (typically healthy heart)
    • 3: Downsloping: signs of unhealthy heart
12. ca - number of major vessels (0-3) colored by flourosopy
    • colored vessel means the doctor can see the blood passing through
    • the more blood movement the better (no clots)
13. thal - thalium stress result 3 = normal; 6 = fixed defect; 7 = reversable defect
    • 1,3: normal
    • 6: fixed defect: used to be defect but ok now
    • 7: reversable defect: no proper blood movement when exercising
14. target - have disease or not (1=yes, 0=no) (=the predicted attribute)

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

Dataset

This dataset was created by QuangNguyen711

Released under MIT

Contents