Implementation of ETL Tool for Data warehousing for Non-Hodgkin Lymphoma (NHL) Cancer in Public Sector, Pakistan

doi:10.30534/ijeter/2021/27972021

Abstract

The study was primarily undertaken to establish the conceptual modeling and implementation of Data Warehousing tools through existing demographic and clinic pathologic features of NHL in Pakistan. A secondary aim was to determine the applicability of the Data warehousing in the cancer domain of Lymphoma disease. In this study, we have implemented ETL tools using open source tools and technologies for making Data warehousing and it’s easy to implement with low cast in the department of health for public sector hospitals in the country.

Highlights

This research focus on the complete implementation of data warehousing in any health sector of Pakistan
In order to make it clearer and easier to understand for any doctor, physician and other related persons, this paper will perform the implementation on a single category of cancer which is lymph node known as lymphoma cancer (Lymphoma is a cancer of the lymphatic system, which is part of the body's germ-fighting network.)
3.METHODOLOGY The important to be explained under following major domains to understand the study for ETL: A.DESIGN OF DWH AND DATA MODEL We intended to set up a data warehouse based on two distinct types of data sources (SQL database file and csv format file) for Lymphoma data registry

Summary

1.INTRODUCTION

This research focus on the complete implementation of data warehousing in any health sector of Pakistan. B. REQUIREMNETS GATHERING This paper mainly required to have a huge amount of data and In fig No 01 shows the on cancer (Lymphoma Cancer Types details) complete detailed overview according literature of any hospital which has the valid dataset of patients suffering from lymph node cancer. In the Fig No. shows the data cube dimensions to be stored in the Data Warehouse, includes patients’ details, hospital/doctor details, disease and Lab information as well. In the first Package (ETL) we extract the data from a delimited (.CSV) file and work with sequence of extraction, Transformation and loading into dimensions and load the keys and measurements into fact table. According to [10]”our results suggest confidence in the correctness of the IDR’s data, i.e. that the integrity of the EHR data was maintained during the IDR’s ETL process.”

METADATA

REPORTS GENERATING SCHEME:

COST COUNT ANALYSIS

4.CONCLUSION