Heart Disease Data Set

Open Dataset
Creators:
Janosi, Andras; Steinbrunn, William; Pfisterer, Matthias; Detrano, Robert
Publication Date:
1988
Data Category:
Dataset Description:
The Heart Disease database is a well-regarded resource in the medical research community, particularly for studies related to cardiovascular conditions. It comprises data from four distinct databases: the Cleveland Clinic Foundation, the Hungarian Institute of Cardiology in Budapest, the V.A. Medical Center in Long Beach, California, and the University Hospital in Zurich, Switzerland. Each of these databases contains patient records with various medical attributes, totaling 76 features. However, most research has focused on a subset of 14 key attributes to diagnose the presence of heart disease. he dataset is relatively small, with each database containing a few hundred records. For example, the Cleveland database includes 303 instances. Given the number of attributes and instances, the dataset's size is minimal, making it easily manageable for analysis without requiring significant storage resources. The data was collected over several years, primarily during the 1980s.

Each patient record in the dataset includes the following 14 attributes commonly used in research:​

  • Age: Age of the patient in years.​
  • Sex: Gender of the patient (1 = male; 0 = female).​
  • Chest Pain Type (cp): Categorical variable indicating the type of chest pain experienced, with values ranging from 0 to 3.​
  • Resting Blood Pressure (trestbps): Resting blood pressure in mm Hg upon hospital admission.​
  • Serum Cholesterol (chol): Serum cholesterol level in mg/dl.​
  • Fasting Blood Sugar (fbs): Binary variable indicating if fasting blood sugar is greater than 120 mg/dl (1 = true; 0 = false).​
  • Resting Electrocardiographic Results (restecg): Categorical variable with values 0 to 2 indicating ECG results.​
  • Maximum Heart Rate Achieved (thalach): Maximum heart rate achieved during exercise.​
  • Exercise-Induced Angina (exang): Binary variable indicating if exercise-induced angina occurred (1 = yes; 0 = no).​
  • ST Depression (oldpeak): ST depression induced by exercise relative to rest.​
  • Slope of the Peak Exercise ST Segment (slope): Categorical variable with values 0 to 2.​
  • Number of Major Vessels Colored by Fluoroscopy (ca): Integer value ranging from 0 to 3.​
  • Thalassemia (thal): Categorical variable indicating blood disorder status (3 = normal; 6 = fixed defect; 7 = reversible defect).​
  • Diagnosis of Heart Disease (target): Integer value ranging from 0 to 4, indicating the presence and severity of heart disease.
Publications Citing This Dataset:
Kouw, W.M., & Loog, M. (2019). A Review of Domain Adaptation without Target Labels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 766-785.
DOI:10.1109/TPAMI.2019.2945942
Ayatollahi, H., Gholamhosseini, L., & Salehi, M. (2019). Predicting coronary artery disease: a comparison between two data mining algorithms. BMC Public Health, 19.
DOI:10.1186/s12889-019-6721-5
Ding, N., & Sadeghi, P. (2019). A Submodularity-based Agglomerative Clustering Algorithm for the Privacy Funnel. ArXiv, abs/1901.06629.
https://arxiv.org/pdf/1901.06629.pdf
Zhang, Z., Guan, Y., Ma, X., & Zhang, L. (2019). ActShare: Sensitive Data Sharing with Reliable Leaker Identification. ArXiv, abs/1907.11833.
https://arxiv.org/pdf/1907.11833.pdf
Ma, H., Guo, X., Ping, Y., Wang, B., Yang, Y., Zhang, Z., & Zhou, J. (2019). PPCD: Privacy-preserving clinical decision with cloud support. PLoS ONE, 14.
DOI:10.1371/journal.pone.0217349
Dhurandhar, A., Shanmugam, K., & Luss, R. (2019). Leveraging Simple Model Predictions for Enhancing its Performance. ArXiv, abs/1905.13565.
https://arxiv.org/pdf/1905.13565.pdf
Nassif, A.B., Mahdi, O., Nasir, Q., AbuTalib, M., & Azzeh, M. (2018). Machine Learning Classifications of Coronary Artery Disease. 2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), 1-6.
DOI:10.1109/ISAI-NLP.2018.8692942
Wang, T. (2018). Gaining Free or Low-Cost Transparency with Interpretable Partial Substitute. arXiv: Learning.
https://arxiv.org/pdf/1802.04346.pdf
Kahtan, H., Zamli, K.Z., Fatthi, W.N., Abdullah, A.B., Abdulleteef, M., & Kamarulzaman, N.S. (2018). Heart Disease Diagnosis System Using Fuzzy Logic. Proceedings of the 2018 7th International Conference on Software and Computer Applications.
DOI:10.1145/3185089.3185118
Ali, S., Jarwar, M.A., & Chong, I. (2018). Design Methodology of Microservices to Support Predictive Analytics for IoT Applications. Sensors (Basel, Switzerland), 18.
DOI:10.3390/s18124226
Wang, T. (2018). Hybrid Decision Making: When Interpretable Models Collaborate With Black-Box Models. ArXiv, abs/1802.04346.
https://export.arxiv.org/pdf/1802.04346
Nashif, S., Raihan, M.R., Islam, M.R., & Imam, M.H. (2018). Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System. World Journal of Engineering and Technology, 06, 854-873.
DOI:10.4236/WJET.2018.64057
Huang, Y. (2018). Mobile Health-monitoring System with Inference, Fall Detection, and Cardiovascular Prediction. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY.
DOI:10.24297/IJCT.V17I2.7621
Sati, N.U. (2018). A Collective Learning Approach for Semi-Supervised Data Classification. Pamukkale University Journal of Engineering Sciences, 24, 864-869.
DOI:10.5505/pajes.2017.44341
Moghaddass, R., & Rudin, C. (2018). Bayesian Patchworks: An Approach to Case-Based Reasoning. ArXiv, abs/1809.03541.
https://arxiv.org/pdf/1809.03541.pdf
Durmus, A., Majewski, S., & Miasojedow, B. (2018). Analysis of Langevin Monte Carlo via Convex Optimization. J. Mach. Learn. Res., 20, 73:1-73:46.
https://arxiv.org/pdf/1802.09188.pdf
Kouw, W.M., & Loog, M. (2018). Target Contrastive Pessimistic Discriminant Analysis. ArXiv, abs/1806.09463.
arXiv:1706.08082
Liu, X., Wang, X., Su, Q., Zhang, M., Zhu, Y., Wang, Q., & Wang, Q. (2017). A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method. Computational and Mathematical Methods in Medicine, 2017.
DOI:10.1155/2017/8272091
Luna, J.M., Eaton, E., Ungar, L.H., Diffenderfer, E.S., Jensen, S.T., Gennatas, E.D., Wirth, M., Simone, C.B., Solberg, T.D., & Valdes, G. (2017). Tree-Structured Boosting: Connections Between Gradient Boosted Stumps and Full Decision Trees. ArXiv, abs/1711.06793.
https://arxiv.org/pdf/1711.06793.pdf
Mills, P. (2017). Accelerating kernel classifiers through borders mapping. Journal of Real-Time Image Processing, 17, 313-327.
DOI:10.1007/s11554-018-0769-9
Salleh, M.N., Samat, N.A., Hussain, K., & Leman, A.M. (2017). A Pragmatic Approach of Data Imputation Using Fuzzy-Based Swarm for Heart Disease Classification.
DOI:10.2991/ceie-16.2017.38
Sáez, C., Robles, M., & García-Gómez, J.M. (2017). Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances. Statistical Methods in Medical Research, 26, 312 - 336.
DOI:10.1177/0962280214545122
Liska, G.R. (2017). Monte Carlo Evaluation of Classification Algorithms Based on Fisher's Linear Function in Classification of Patients With CHD. IOSR Journal of Mathematics, 13, 104-109.
DOI:10.9790/5728-130104104109
Kouw, W.M., & Loog, M. (2016). On regularization parameter estimation under covariate shift. 2016 23rd International Conference on Pattern Recognition (ICPR), 426-431.
DOI:10.1109/ICPR.2016.7899671
Christopher, J.J., Nehemiah, H.K., & Kannan, A. (2016). Knowledge-based Systems and Interestingness Measures: Analysis with Clinical Datasets. J. Comput. Inf. Technol., 24, 65-78.
DOI:10.20532/CIT.2016.1002500
Variables:
Details:
health heart conditions
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