Open AccessModeling Method for Leveraging Data Quality in Healthcare Big Data
Author(s) -
Madhu H. K.*,
Dharavath Ramesh
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.e2528.039520
Subject(s) - big data , leverage (statistics) , computer science , health care , data science , data quality , quality (philosophy) , data mining , process (computing) , analytics , data modeling , data analysis , risk analysis (engineering) , artificial intelligence , database , engineering , medicine , metric (unit) , philosophy , operations management , epistemology , economics , economic growth , operating system
An accurate diagnosis of the healthcare-based Big Data will always demand a significant level of quality in its input data itself, which is a serious level of concern in the area of healthcare analytics. Review of existing approaches shows that there has been various learning-based approaches being used for disease diagnosis which often ignores various issues viz. data aggregation, presence of error prone data, accuracy etc. Therefore, this paper presents a novel framework which offers cost effective modeling of the aggregation process of healthcare-big data followed by facilitating solution towards identifying and rectifying all the positions within a database system where there are presence of an error. The proposed system offer a mechanism where the error-prone data has been identified and substituted with data of better quality in order to offer better analytical outcomes. The study offers a strong baseline in order to leverage the data quality in healthcare big data.