# Including Interaction Terms Or Not?

I am currently building a predictive, logistic model. The outcome is whether or not one has a disease (yes/no). The predictors are smoking status (binary predictor) and percentage (continuous variable).

I am confused about the interpretation after adding an interaction term and would be grateful for all the help.

Can someone please help me interpret the interaction term for me? For example, do you need to state the odds ratio of this interaction is after adjusting for percentage and smoking status (this doesn't seem right)?

Call:
glm(formula = disease ~ percentage + SmokingNA + percentage:SmokingNA,
family = binomial, data = final)

Deviance Residuals:
Min       1Q   Median       3Q      Max
-2.2361  -1.0196   0.4236   0.8969   1.6511

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept)                  1.81383    0.96998   1.870   0.0615 .
percentage                  -0.06994    0.03754  -1.863   0.0625 .
SmokingNAsmoking            -2.25392    1.45208  -1.552   0.1206
percentage:SmokingNAsmoking  0.13922    0.05922   2.351   0.0187 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 61.513  on 46  degrees of freedom
Residual deviance: 51.366  on 43  degrees of freedom
AIC: 59.366

Number of Fisher Scoring iterations: 5

Here is my data:

structure(list(percentage = c(5.5, 72.1, 7.9, 80.6, 56.3, 11.5,
15.3, 12.3, 30.9, 27.5, 0.3, 5.3, 19.6, 19.8, 0.3, 40.5, 16.8,
38, 13.8, 29.9, 15.8, 15.3, 22.8, 17.2, 41.2, 17.2, 31.6, 41.2,
19.6, 38, 41.2, 29.9, 15.3, 29.9, 38, 30.9, 31.6, 15.3, 15.3,
38, 31.6, 41.3, 21.4, 0.4, 41.2, 7.6, 29.9),
SmokingNA = structure(c(2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L), .Label = c("non-smoking",
"smoking"), class = "factor"), disease = structure(c(1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L,
1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L), .Label = c("none", "disease"), class = "factor")), row.names = c(NA,
-47L), class = "data.frame")
>

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