This document summarizes a study exploring the prevalence of asthma in the United States in 2017. It presents exploratory analysis of the relationships between asthma and variables like age, sex, race, income, and geography. A logistic regression model is built to predict asthma likelihood using these variables. The model achieves an accuracy rate of 84% and area under the ROC curve of 0.6695 when tested on a separate dataset, showing logistic regression can reasonably classify individuals as asthmatic or non-asthmatic. Challenges from the survey method and missing data are also noted.

1. US ASTHMA PREVALENCE 2017
EXPLORATORY ANALYSIS AND PREDICTIVE MODELING
ANH DO – SPRING 2019

2. AGENDA
1. INTRODUCTION
2. EXPLORATORY ANALYSIS
3. LOGISTIC REGRESSION MODEL
4. CONCLUSION
5. CHALLENGES

3. INTRODUCTION
SUMMARY OF PREVIOUS RESEARCH
HTTPS://ANHSTUDIESDATA.SHINYAPPS.IO/USASTHMASTUDY2017/

4. THE DATASET
• SOURCE: LANDLINE/CELLULAR SURVEY BY CDC IN 2017
• 450,016 OBSERVATIONS – 358 VARIABLES
• SELECTED 11 VARIABLES

5. ASTHMA PREVALENCE WITH RESPECT TO
• AGE GROUP
• SEX
• RACE
• INCOME GROUP
• EDUCATION LEVEL
• WEIGHT GROUP
• SMOKING STATUS
• DEPRESSIVE DISORDER
• PHYSICAL ACTIVENESS

6. GEOGRAPHIC DISTRIBUTION - COUNT
Largest count of asthma:
• Kansas
• Florida
Smallest count of asthma:
• Alaska
• Nevada

7. GEOGRAPHIC DISTRIBUTION - PROBABILITY
Largest Probability:
• West Virginia
Smallest Probability:
• Minnesota

8. BINARY LOGISTIC REGRESSION
PREDICT THE LIKELIHOOD OF A PERSON HAVING ASTHMA USING ALL VARIABLES

9. FULL MODEL WITH FULL DATASET
• ASTHMA STATUS: CHANGED TO BINARY
• 0: DOES NOT CURRENTLY HAVE ASTHMA
• 1: CURRENTLY HAVE ASTHMA
• CHANGE ALL 9 PREDICTORS TO FACTOR
VARIABLES
glm (AsthmaStatus ~. , family = binomial (link = ‘logit’), data = data)

10. COEFFICIENT INSPECTION

11. MACHINE LEARNING
• PARTITION: 60% TRAIN, 40% TEST
• EVALUATED BY CONFUSION MATRIX AND ROC CURVE

12. RECEIVER OPERATING CHARACTERISTICS CURVE
Area under the ROC curve" "0.66954885547759

13. CONFUSION MATRIX
PREDICTED
ACTUAL
NO YES
NO 106628 8827
YES 12214 3572
Accuracy Rate Error Rate Sensitivity Specificity False Positive
0.84 0.16 0.29 0.90 0.10
CLASSIFICATION THRESHOLD: 0.16

14. CONCLUSION
LOGISTIC REGRESSION CAN BE USED TO CLASSIFY ASTHMATIC AND NON-ASTHMATIC
HTTPS://ANHSTUDIESDATA.SHINYAPPS.IO/USASTHMASTUDY2017/

15. CHALLENGES
• LANDLINE AND CELLULAR PHONE SURVEY – BIASED SAMPLE
• RANDOM MISSING DATA REMOVED FOR ALGORITHM

16. THANK YOU