• Senthil Kumar Ramadoss
• Puviarasan N

While increasing amounts of data exist in medical databases and multiple variables are measured for each case in a data set, there exists an underlying relationship between all pairs of variables, some highly correlated and some not. This report describes a technique that creates networks of related variables, or relevance networks, by dropping links with either too weak correlation or too few data points to defend the relationship. The paper describes how applying this methodology to the domain of laboratory results allows the generation of meaningful relations between types of laboratory tests. These relations could be used as the basis of further exploratory research.

Database Knowledge Discovery, Medical Database, Relevance Networks, Data Mining, knowledge Discovery Models, Strategic knowledge Discovery.

"USE OF RELEVANCE NETWORKS IN KNOWLEDGE DISCOVERY FROM MEDICAL DATABASES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 2, page no.92-97, February-2021, Available :http://www.jetir.org/papers/JETIR2102306.pdf

"USE OF RELEVANCE NETWORKS IN KNOWLEDGE DISCOVERY FROM MEDICAL DATABASES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 2, page no. pp92-97, February-2021, Available at : http://www.jetir.org/papers/JETIR2102306.pdf