Abstract Views :195 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science and Engineering, K.S. Rangasamy College of Technology, Tamilnadu, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 2 (2012), Pagination: 82-86
Abstract
The Machine Learning (ML) field has gained its momentum in almost any domain of research and just recently has become a reliable tool in the medical domain. The empirical domain of automatic learning is used in tasks such as medical decision support, protein-protein interaction, extraction of medical knowledge, and for overall patient management care. ML is envisioned as a tool by which computer-based systems can be integrated in the healthcare field in order to get a better, more efficient medical care. This paper describes a ML-based methodology for building an application that is capable of identifying and disseminating healthcare information. It identifies semantic relations that exist between diseases and treatments. This paper tells a platform to enhance effectiveness and efficiency of home monitoring using data mining for early detection of any worsening in patient's condition. These worsening could require more complex and expensive care if not recognized. Disease management programs, which use no advanced information and computer technology, are as effective as telemedicine but more efficient because less costly. The platform improved home monitoring by adding data mining functionalities. This was important in order to improve home monitoring effectiveness and efficiency, especially benchmarking telemedicine to other disease management programs, and not only to ambulatory follow-up.
Keywords
Data Mining (DM), Heart Failure (HF), Heart Rate Variability (HRV), Home Monitoring (HM).
