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PHARM - Association Rule Mining for Predictive Health
Chih-Wen Cheng1, Greg S Martin2, Po-Yen Wu1
1School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
IFMBE Proceedings
|September 3, 2016
Summary
Predictive health systems can now offer objective health assessments. This study developed PHARM, a system using data mining to predict mental illness from psychological factors, aiding preventative care.
Area of Science:
- Healthcare Informatics
- Data Mining
- Predictive Analytics
Background:
- Predictive health focuses on maintaining wellness over treating illness.
- Computer-based decision support systems enhance quantitative health assessment for objective advice.
- Data mining for predictive health presents unique challenges, limiting current system development.
Purpose of the Study:
- To research and develop an interactive decision support system named PHARM.
- To integrate association rule mining for objective health assessment and prediction.
- To collaborate with the Emory Center for Health Discovery and Well Being (CHDWB®).
Main Methods:
- Utilized association rule mining techniques.
- Developed an interactive decision support system (PHARM).
- Conducted a case study involving psychological factors and mental illness prediction.
Main Results:
- Generated 12 quantitative and objective rules for health prediction.
- Successfully predicted mental illness based on five psychological factors.
- Demonstrated the system's value in preventing potential illness.
Conclusions:
- The PHARM system offers valuable, usable decision support for predictive health.
- The system enables prioritization of advice and action plans for disease risk reduction.
- Highlights the potential of data mining in advancing proactive healthcare models.
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