Volume 11 - Volume 11
Advanced Filter Based Machine Learning Models on Clinical Databases for Outlier Detection
Abstract
Feature selection approaches are used to improve the efficiency of the clinical databases in the
machine learning classification. Since, most of the conventional feature selection and classification
approaches are difficult to handle high dimensionality for pattern evaluation. Also these models are
difficult to filter noise on different heterogeneous features. In this work, a hybrid data transformation
and outlier detection methods are developed on the clinical databases to improve the classification
accuracy. Experimental results show that the present model has better accuracy in evaluating the
accuracy than the conventional models on clinical databases.
Paper Details
PaperID: 2003
Author's Name: V. Devi Satya Sri and Srikanth Vemuru
Volume: Volume 11
Issues: Volume 11
Keywords: Clinical Databases, Machine Learning, Classification.
Year: 2021
Month: May
Pages: 1178-1199