A Knowledge-Based Data Mining System for Diagnosing Malaria Related Cases in Healthcare Management
Olugbenga Oluwagbemi 1, Uzoamaka Ofoezie2 , Nwinyi,Obinna 3
1Rochester Institute of Technology, 28 Lomb Memorial Drive, Rochester NY 14623, Rochester , New York, USA 2 (Bioinformatics Unit) Departments of Computer and Information Sciences School of Natural and Applied Sciences College of Science and Technology, Covenant University, Ogun State, Nigeria.
3Department of Biological sciences, School of Natural and Applied Sciences College of Science and Technology, Covenant University, Ogun State, Nigeria.
Data mining a process for assembling and analyzing data into useful information can be applied as rapid measures for malaria diagnosis. In this research work we implemented (knowledge-base) inference engine that will help in mining sample patient records to discover interesting relationships in malaria related cases. The computer programming language employed was the C#.NET programming language and Microsoft SQL Server 2005 served as the Relational Database Management System (RDBMS). The results obtained showed that knowledge-based data mining system was able to successfully mine out and diagnose possible diseases corresponding to the selected symptoms entered as query. With this finding, we believe the development of a Knowledge-based data mining system will not only be beneficial towards the diagnosis of malaria related cases in a more cost effective means but will assist in crucial decision making and new policy formulation in the malaria endemic regions.
Keywords: diagnosis, data mining, malaria
Data mining as a process for analyzing data from different perspectives and summarizing it into useful information can generate information that can be used to increase revenue, cut costs, or both . Data mining identifies trends within data that go beyond simple analysis. Through the use of sophisticated algorithms, non-statistician users have the ability to identify key attributes of business processes and target opportunities . Data mining refers to extracting or “mining” knowledge from large amounts of data . In some hospitals in Nigeria, it is difficult to select or extract very important information from the database because it is practically cumbersome and this makes it increasingly difficult to support decision making and also to detect abnormal patterns of disease enlistments. Traditionally, analysts have performed the task of extracting useful information from recorded data. But, the increasing volume of data in modern business and science most especially the health sector calls for computer-based approaches. Data mining is the process of applying computer-based methodology, including new techniques for knowledge discovery, from data.
The aim of this research work is to implement an algorithm that will help in mining large patient records to discover interesting relationships in malaria related cases, which will assist in crucial decision making and new policy formulation. This aim will be achieved through the following objectives; (i) assist a doctor in detecting abnormal patterns in the data base of the patients’ records, (ii) Increase time efficiency of physicians by reducing time spent on documentation and time spent sorting through the large database during and after shifts, (iii) capture comprehensive data on patient for clinical research, (iv) to provide an insight into the symptom patterns among patients of the hospital, (v) to provide the Hospital Authorities with data mining solutions and services to allow them understand health related behaviors and the pathologies encountered.
2. Relevant work
Several research efforts have been made on the control of malaria. A recent study discussed how information or data is being targeted efficiently and effectively to control and justifies investment in research of...
References:  Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, 2001, Morgan
 RI Chima, CA Goodman, A Mills (2003), The economic impact of malaria in Africa: a critical review of the evidence, Health Policy, Volume 63, Issue 1, January 2003, Pages 17-36.
 M Llinás, HA del Portillo, (2005), Mining the malaria transcriptome, Trends in parasitology, Volume 21, Issue 8, August 2005, Pages 350-352.
 X Zhou, Y Peng, B Liu, (2010), Text mining for traditional Chinese medical knowledge discovery: A survey, Journal of Biomedical Informatics, 2010 (Article in press).
 Leszek Maciaszek and Bruc Lee Liong, Practical Software Engineering: A Case Study, (2005), ISBN 0321 20465 4
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