Abstract
The healthcare environment is generally perceived as being ‘information rich’ yet ‘knowledge poor’. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. Knowledge discovery and data mining have found numerous applications in business and scientific domain. Valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, we briefly examine the potential use of classification based data mining techniques such as Rule based, decision tree and Artificial Neural Network to massive volume of healthcare data. In particular we consider a case study using classification techniques on a medical data set of diabetic patients.
Highlights
It is well known that in Information Technology (IT) driven society, knowledge is one of the most significant assets of any organization
Classification data mining techniques: We describe a few Classification data mining techniques with illustrations of their applications to healthcare
Data mining of diabetes data: We present a case study of application of data mining and analyze data of children with Diabetes mellitus and Diabetes insipidus
Summary
It is well known that in Information Technology (IT) driven society, knowledge is one of the most significant assets of any organization. Pragmatic use of Database systems, Data Warehousing and Knowledge Management technologies can contribute a lot to decision support systems in health care. Data mining is the core step, which results in the discovery of hidden but useful knowledge from massive databases.
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