Open AccessPredicting the Heart Disease's using Machine Learning Techniques
Author(s) -
V. V. S. S. S. Balaram
Publication year - 2019
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit195420
Subject(s) - confidentiality , risk analysis (engineering) , informatics , business , public health , health care , medical emergency , internet privacy , health informatics , patient confidentiality , population , computer security , medicine , computer science , environmental health , engineering , nursing , political science , electrical engineering , law
The research also explores ways to protect online identities of patients from disclosure or privacy concerns). We will address the situation of the patient like situation of heart problem that experience life-threatening emergencies. With adequate lead time, patients and doctors can avert serious emergencies from occurring. Since handling of serious emergencies is particularly expensive, the proposed technologies can potentially reduce the overall cost of healthcare delivery and management in rural populations. Implement solutions that assure confidentiality, security and integrity while maximizing theAvailability of information for public health use and strategically integrate clinical health, environmental risk and population health informatics.