Dependence of Body Mass Index on Some Dietary Habits: An Application of Classification and Regression Tree

The purpose of this study was to determine the influence of some eating habits on body mass index (BMI) using a regression model created via the classification and regression tree method (CART).

The study was conducted using a questionnaire specially developed for the study, evaluated for reliability and validity. In addition to demographics (age and sex), the questions concern the timing of the meals and the type of food consumed. The data contains records for 533 people (322 women and 211 men) aged 18 to 65 years. The survey was conducted in the period 2019-2021 in Stara Zagora, Bulgaria. Data were processed using descriptive statistics, and regression and classification data mining method CART.

A CART model with a dependent variable BMI and predictors Sex, Age, Breakfast type, Breakfast time, Lunchtime, Lunch type, Dinner time, Dinner type have been created. The obtained model is statistically significant at a significance level of P<0.0001 and a coefficient of determination R² = 0.495. The normalized importance of the factors that affect the BMI is as follows: Sex (100%), Age (61.4%), Lunch type (26. 0%), Lunchtime (18.8%), Dinner time (13.9%), and Breakfast type (13.2%). Women have a lower BMI than men. BMI increases with age.

The CART method allows to make a classification by the predictors used and gives opportunities for a more in-depth analysis of the reasons for the increase in BMI. The level of influence of diet and eating habits (type of food, time of consumption) on BMI was determined.