Table 1.

Descriptions and details of the variables included in the machine learning algorithms.

Variable	Types of variable	Description
Main residence	Categorical variablea	Urban area (0), rural area (1)
Menopausal status	Categorical variable	Premenopause (0), postmenopause (1)
Age in years	Discrete variable	Age at breast cancer diagnosis or screening
BMI (kg/m2)	Continuous variable	BMI at breast cancer diagnosis or screening
Age of menarche	Discrete variable	Age at first menstruation
Duration of reproductive life span	Discrete variable	Premenopausal women: current age – age of menarche; postmenopausal women: menopause age – age of menarche
Pregnancy history	Categorical variable	No (0), yes (1)
Number of live births	Discrete variable	Live births is defined as births of children who showed any sign of life
Age at first birth	Discrete variable	Age of women at birth of first child (for women with no live birth, this value equals 99)
Family history of breast cancer	Categorical variable	First-degree or second-degree female relatives had breast cancer: no (0), yes (1)
Case-control status (outcome variable)	Categorical variable	Control (0), case (1)

^aCategorical variables were converted into one-hot encoding before being provided to machine learning algorithms.