Data Mining Based Predictive Models for Overall Health Indices
2010-04-14
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Data Mining Based Predictive Models for Overall Health Indices
Published Date
2010-04-14
Publisher
Type
Report
Abstract
In this study, we infer health care indices of individuals using their pharmacy medical and prescription claims. Specifically, we focus on the widely used Charlson Index. We use data mining techniques to formulate the problem of classifying Charlson Index (CI) and build predictive models to predict individual health index score. First, we present comparative analyses of several classification algorithms. Second, our study shows that certain ensemble algorithms lead to higher prediction accuracy in comparison to base algorithms. Third, we introduce cost-sensitive learning to the classification algorithms and show that the inclusion of cost-sensitive learning leads to improved prediction accuracy. The built predictive models can be used to allocate health care resources to individuals. It is expected to help reduce the cost of health care resource allocation and provisioning and thereby allow countries and communities lacking the ability to afford high health care cost to provide health indices (coverage), provide individuals with health index which takes into consideration their overall health and thereby improve quality of individual health assessment (quality), and improve reliability of decision making by focusing on a set of objective criteria for all individuals (reliability).
Keywords
Description
Related to
Replaces
License
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Suggested citation
Rajkumar, Ridhima; Shim, Kyong Jin; Srivastava, Jaideep. (2010). Data Mining Based Predictive Models for Overall Health Indices. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215827.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.