Abstract
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.
| Original language | American English |
|---|---|
| Title of host publication | 2015 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 245-249 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781467383257 |
| DOIs | |
| State | Published - 2015 |
| Event | 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015 - Boston, United States Duration: Oct 13 2015 → Oct 17 2015 |
Publication series
| Name | 2015 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015 |
|---|
Conference
| Conference | 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015 |
|---|---|
| Country/Territory | United States |
| City | Boston |
| Period | 10/13/15 → 10/17/15 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
ASJC Scopus subject areas
- Health Policy
- Health Information Management
- Computer Networks and Communications
- Computer Science Applications
- Health Informatics
- Surgery
- Health(social science)
Keywords
- Classification
- Data mining
- Machine learning
- Matrix factorization
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