A feature-based selection technique for reduction of large scale data

The inflated development in public healthcare domain has forced numerous organisations to construct and maintain large scale databases or data warehouses. However, the prediction of knowledge should be an automated process to discover hidden information from large scale databases. The elaborated studies in the past suggest that minimum interesting variables can determine qualified information while preserving information among the data. In addition, it is determined that large scale databases usually comprise of redundant and irrelevant features which have proven to be a major setback for efficient and effective analysis of data. This paper intends to provide an integrated approach by utilising machine learning technique and other convention statistical techniques for extraction of information from large scale databases. In the formulated approach, we have potentially exploited two approaches where the first approach emphasises on retrieval of feature subsets using MODTree filtering technique from discretised datasets with relative application domain on real datasets of Substance Abuse and Mental Health Data Archive (SAMHDA) collected from different states of USA. The second phase of study exploits statistical techniques on potential targets for discovery of interesting information from reduced datasets. We present a novel perspective using feature selection and statistical techniques for determination of knowledge from large scale databases.