Celia Kn Sen
Biographic Data
| ID | 1561379 |
|---|---|
| NAME | Celia Kn Sen |
| GIVEN NAMES | Celia Kn |
| FAMILY NAME | Sen |
| SIGNATURE | SEN C K |
| AFFILIATIONS | Koç University |
| VERIFIED | No |
| TOTAL WORKS | 1 |
| TOTAL CITATIONS | 2 |
| WORKS AS AUTHOR | 1 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2016 |
| H-INDEX | 1 |
Who does not get screened? A simple model of the complex relationships in mammogram non-attendance
With increasing mammogram rates, identifying attributes of non-attending women entails going beyond differences in demographic groups to reveal complex interactions among personality attributes. In this study, we analyzed survey data from 474 women aged 41 years and older using decision trees. By incorporating personality, religiousness, and age, we were able to correctly classify 42.9 percent of non-attenders compared to 4.4 percent with logisti…
Who does not get screened? A simple model of the complex relationships in mammogram non-attendance
With increasing mammogram rates, identifying attributes of non-attending women entails going beyond differences in demographic groups to reveal complex interactions among personality attributes. In this study, we analyzed survey data from 474 women aged 41 years and older using decision trees. By incorporating personality, religiousness, and age, we were able to correctly classify 42.9 percent of non-attenders compared to 4.4 percent with logisti…
Who does not get screened? A simple model of the complex relationships in mammogram non-attendance
With increasing mammogram rates, identifying attributes of non-attending women entails going beyond differences in demographic groups to reveal complex interactions among personality attributes. In this study, we analyzed survey data from 474 women aged 41 years and older using decision trees. By incorporating personality, religiousness, and age, we were able to correctly classify 42.9 percent of non-attenders compared to 4.4 percent with logisti…
Artificial Intelligence (1 work) · Attendance (1 work) · Clinical Psychology (1 work) · Computer Science (1 work) · Decision tree (1 work) · Global Cancer Incidence and Screening (1 work) · Health disparities and outcomes (1 work) · Intervention (counseling (1 work) · Logistic regression (1 work) · Machine learning (1 work)