Comparison of prediction of obesity status based on different machine learning approaches with different factor quantities

Contemporarily, obesity, as a chronic disease, has seriously affected the quality of daily life and attracted the attention of scholars. This research will investigate 2111 observations on eating habits and behaviour patterns of obesity based on five machine learning approaches (Decision Tree (DT), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Random Forest (RF) and Extremely Randomized Trees (ERT) models). By adding 16 factors in the data into five models successively according to their correlation with obesity level from strong to weak, one explores the impact of different factor quantities on the prediction of obesity status. According to the three metrics of accuracy score, Kappa score and Matthews correlation coefficient, the classification effect of the five models tended to be stable and saturated when the quantity of factors reached a certain value. Additionally, the accuracy of the model classification will be greatly improved when the correlation coefficients between the new factor and the added factors are small. Meanwhile, among the five models, XGBoost model has the best classification effect under 14 factors, with an accuracy of 97.16%. The research of this paper further explores the classification effects of different models on obesity status under different factor quantities. Overall, these results shed light on guiding the related research of the internal relationship between different factor quantities and the classification effect of different machine learning approaches.