Applying matrix factorization in data reconstruction for heart disease patient classification

Heart disease is one of the most severe health illnesses. Developing accurate and efficient methods to diagnose heart disease is crucial in providing good heart healthcare to patients. In this paper, a data mining based technique for diagnosing heart disease is introduced, in which heart disease related patient data sets are utilized. A matrix factorization based technique for missing data reconstruction is presented. Numerical results show that recovery data sets are able to achieve reliable diagnosis or classification performance comparable to using original completed patient datasets.

[1]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

[2]  James M. Keller,et al.  A fuzzy K-nearest neighbor algorithm , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[4]  Xian-Da Zhang,et al.  Matrix Analysis and Applications , 2017 .

[5]  Qihui Wu,et al.  Kernel-Based Learning for Statistical Signal Processing in Cognitive Radio Networks: Theoretical Foundations, Example Applications, and Future Directions , 2013, IEEE Signal Processing Magazine.

[6]  Xiaodong Wang.  Blind Multiuser Detection , 2003 .

[7]  Nils J. Nilsson,et al.  Introduction to Machine Learning , 2020, Machine Learning for iOS Developers.

[8]  Upamanyu Madhow,et al.  Blind adaptive multiuser detection , 1995, IEEE Trans. Inf. Theory.

[9]  Abdulkadir Sengür,et al.  Effective diagnosis of heart disease through neural networks ensembles , 2009, Expert Syst. Appl..

[10]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[11]  Gaj Vidmar,et al.  Outlier Detection for Healthcare Quality Monitoring – A Comparison of Four Approaches to Over‐Dispersed Proportions , 2014, Qual. Reliab. Eng. Int..

[12]  Dongkyoo Shin,et al.  Effective Diagnosis of Heart Disease through Bagging Approach , 2009, 2009 2nd International Conference on Biomedical Engineering and Informatics.

[13]  Zhu Han,et al.  Catching Attacker(s) for Collaborative Spectrum Sensing in Cognitive Radio Systems: An Abnormality Detection Approach , 2010, 2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN).

[14]  Jill P. Mesirov,et al.  Support Vector Machine Classification of Microarray Data , 2001 .