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nezahatkorkmaz/heart-disease-dataset

❤️ 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… See the full description on the dataset page: https://huggingface.co/datasets/nezahatkorkmaz/heart-disease-dataset.

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❤️ 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 modeling and more robust exploratory data analysis.

📊 Original Features

ColumnDescription
ageAge of the patient (years)
sexGender (1 = male, 0 = female)
cpChest pain type (1 = typical angina, 2 = atypical angina, 3 = non-anginal pain, 4 = asymptomatic)
trestbpsResting blood pressure (mm Hg)
cholSerum cholesterol (mg/dl)
fbsFasting blood sugar > 120 mg/dl (1 = true, 0 = false)
restecgResting electrocardiographic results (0 = normal, 1 = ST-T abnormality, 2 = left ventricular hypertrophy)
thalachMaximum heart rate achieved
exangExercise-induced angina (1 = yes, 0 = no)
oldpeakST depression induced by exercise
slopeSlope of the peak exercise ST segment (1 = upsloping, 2 = flat, 3 = downsloping)
caNumber of major vessels colored by fluoroscopy
thalThalassemia (3 = normal, 6 = fixed defect, 7 = reversible defect)
numDiagnosis of heart disease (0 = no disease, 1-4 = disease severity levels)

🧠 Engineered Features

FeatureDescription
age_groupAge category (30s, 40s, 50s, 60s)
cholesterol_levelCholesterol status (low, normal, high)
bp_levelBlood pressure status (low, normal, high)
risk_scoreComposite risk score: (age * chol / 1000 + trestbps / 100)
symptom_severitySeverity index based on symptoms: (cp * oldpeak)
log_cholLogarithm of cholesterol level
log_trestbpsLogarithm of resting blood pressure
age_squaredSquare of patient age
chol_squaredSquare of cholesterol level
age_thalach_ratioRatio of max heart rate to age (plus 1)
risk_factorAdvanced risk factor: (cp * oldpeak * thal)
missing_valuesCount of missing values in ca and thal
chol_trestbps_ratioRatio of cholesterol to resting BP
log_thalach_cholLog of (heart rate × cholesterol)
symptom_zscoreZ-score of symptom severity
avg_chol_by_age_groupAverage cholesterol for corresponding age group
thalach_chol_diffDifference between max heart rate and cholesterol
symptom_severity_diffDeviation from average symptom severity by age group
age_chol_effectProduct of age and cholesterol
thalach_risk_effectProduct of max heart rate and risk score
age_trestbps_effectProduct of age and resting BP
chol_risk_ratioRatio of cholesterol to risk score

💡 Use Cases

  • —Cardiovascular risk prediction using machine learning
  • —Exploratory data analysis for medical research
  • —Feature selection and dimensionality reduction tutorials
  • —Educational purposes in data science and healthcare analytics
  • —Model interpretability studies using engineered variables

📁 File Info

  • —Format: CSV or DataFrame (depending on hosting)
  • —Instances: ~300 patients
  • —License: MIT (Open Source)
  • —Source: Based on UCI Heart Disease Dataset + custom engineered features

📬 Citation & Contact

If you use this dataset in your research or application, please consider citing the original UCI dataset and giving credit to this enhanced version's author.

Author: nezahatkk GitHub: https://github.com/nezahatkk