1. Using SAS Predictive Modeling to Investigate the Asthma’s Patient Future hospitalization Risk. Yehia H. Khalil, University of Louisville, Louisville, KY,US presented by: Xxxxxxx DSCI 5240

2. Aim Develop a predictive model to forecast future Asthma hospitalization Asthma A chronic inflammatory disorder of the airways 21 million Americans diagnosed Hospitalization rate growing (more than a million cases a year) Costs for Asthma: $14 billion

3. Predictive modeling Ability to incorporate any type of variable into analysis Dynamic; can easily accommodate any information to adjust model SAS SEMMA methodology Sample Explore Modify Model Access

4. Source of 2009 Dataset Medical Expenditure Panel Survey California Health Interview Survey Survey 47,614 adults 3,379 adolescents 8,945 children

5. Useful Parameters Demographics: age, race, marital status Health Behaviors: physical activities, fast food, alcohol consumption Health Conditions other than Asthma Health Insurance Poverty Level Emergency preparedness module: medication Mental or Emotional Condition

8. Fig. 4 Analysis Diagram note: 40% training 30% testing 30% validation

12. Conclusion General health conditions, psychological distress and poverty level affect future hospitalization risk Rx coverage and patient disability influence taking medication regularly and can increase future hospitalization risk It is possible to enhance interventions, programs and alternatives to avoid future hospitalizations