Data Set Information:

每个患者可能分为两类：冠心病或正常。如果患者的直径变窄大于或等于50%，则将其归类为CAD，否则视为正常。

Attribute Information:

Z-Alizadeh Sani数据集包含303名患者的记录，每个患者有54个特征。这些特征分为四组：人口统计学、症状和检查、心电图、实验室和回声特征。

Relevant Papers:

R. Alizadehsani, J. Habibi, M. J. Hosseini, H. Mashayekhi, R. Boghrati, A. Ghandeharioun, et al., 'A data mining approach for diagnosis of coronary artery disease,' Computer Methods and Programs in Biomedicine, vol. 111, pp. 52-61, 2013/07/01/ 2013.

R. Alizadehsani, J. Habibi, B. Bahadorian, H. Mashayekhi, A. Ghandeharioun, R. Boghrati, et al., 'Diagnosis of Coronary Arteries Stenosis Using Data Mining,' Journal of Medical Signals and Sensors, vol. 2, pp. 153-159, Jul-Sep

R. Alizadehsani, M. J. Hosseini, Z. A. Sani, A. Ghandeharioun, and R. Boghrati, 'Diagnosis of Coronary Artery Disease Using Cost-Sensitive Algorithms,' in 2012 IEEE 12th International Conference on Data Mining Workshops, 2012, pp. 9-16.

Z. Arabasadi, R. Alizadehsani, M. Roshanzamir, H. Moosaei, and A. A. Yarifard, 'Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm,' Computer Methods and Programs in Biomedicine, vol. 141, pp. 19-26, 2017/04/01/ 2017.

R. Alizadehsani, J. Habibi, Z. Alizadeh Sani, H. Mashayekhi, R. Boghrati, A. Ghandeharioun, et al., 'Diagnosing Coronary Artery Disease via Data Mining Algorithms by Considering Laboratory and Echocardiography Features,' Research in Cardiovascular Medicine, vol. 2, pp. 133-139, 07/31

R. Alizadehsani, J. Habibi, M. J. Hosseini, R. Boghrati, A. Ghandeharioun, B. Bahadorian, et al., 'Diagnosis of coronary artery disease using data mining techniques based on symptoms and ecg features,' European Journal of Scientific Research, vol. 82, pp. 542-553, 2012.

R. Alizadehsani, M. H. Zangooei, M. J. Hosseini, J. Habibi, A. Khosravi, M. Roshanzamir, et al., 'Coronary artery disease detection using computational intelligence methods,' Knowledge-based Systems, vol. 109, pp. 187-197, 2016/10/01/ 2016.

R. Alizadehsani, J. Habibi, Z. A. Sani, H. Mashayekhi, R. Boghrati, A. Ghandeharioun, et al., 'Diagnosis of Coronary Artery Disease Using Data mining based on Lab Data and Echo Features,' Journal of Medical and Bioengineering, vol. 1, 2012.

A. Roohallah, H. Mohammad Javad, B. Reihane, G. Asma, K. Fahime, and S. Zahra Alizadeh, 'Exerting Cost-Sensitive and Feature Creation Algorithms for Coronary Artery Disease Diagnosis,' International Journal of Knowledge Discovery in Bioinformatics (IJKDB), vol. 3, pp. 59-79, 2012.

R. Alizadehsani, M. J. Hosseini, Z. Alizadehsani, M. H. Mohammadi, O. Barati, and F. Khozeimeh, 'System for determining the need for Angiography in patients with symptoms of Coronary Artery disease,' ed: Google Patents, 2014.

F. Babi??, J. Olej??r, Z. Vantov??, and J. Parali??, 'Predictive and Descriptive Analysis for Heart Disease Diagnosis,' presented at the Federated Conference on Computer Science and Information Systems, 2017.

LOHITA, Kodali et al. Performance Analysis of Various Data Mining Techniques in the Prediction of Heart Disease. Indian Journal of Science and Technology, [S.l.], dec. 2015. ISSN 0974 -5645. Available at: <[Web link]>. Date accessed: 17 Nov. 2017. [Web link].

J. Bekta??, T. Ibrik?§i, and I. ?–zcan, 'Classification of Real Imbalanced Cardiovascular Data Using Feature Selection and Sampling Methods: A Case Study with Neural Networks and Logistic Regression,' International Journal on Artificial Intelligence Tools, 2017.

C. Yadav, S. Lade, and M. K. Suman, 'Predictive Analysis for the Diagnosis of Coronary Artery Disease using Association Rule Mining,' International Journal of Computer Applications, vol. 87, 2014.