Dietary Habits and Daily Routines as Prognostic Factors in Endometrial Cancer: A Machine Learning Approach

Abstract

Endometrial cancer (EC) is becoming more common worldwide, primarily due to an increase in life expectancy and obesity. As several modifiable factors may affect EC incidence and progression, we aimed to elucidate how dietary habits and daily routines influence recurrence and survival among women with EC, using a Random Survival Forest (RSF) approach. 481 women who previously underwent hysterectomy due to EC completed two extensive questionnaires on dietary habits and daily routines, and we used RSF to identify risky or protective variables. Among the 186 variables considered, consumption of sugar-sweetened beverages and fried potatoes increased the risk of EC recurrence and death, while physical activity decreased the risk of death. We conclude that RSF is a suitable approach to study survival in multivariable datasets.