Abstract
To detect and prescribe for individuals potentially failing to maintain their weight after weight loss, it is essential to predict how much weight individuals will maintain in the future (weight maintenance rate) and provide interpretable insights about factors behind the predicted failure of weight maintenance. To this end, this paper proposes a weight maintenance rate prediction method with high interpretability and accuracy. Using statistical causal discovery DirectLiNGAM, this method captures causalities among variables to make prediction results interpretable and selects direct causal variables as prediction features to estimate weight maintenance rate accurately. We evaluated our method on the observational data of our weight loss study for 140 subjects over 8 weeks collecting weight, physical activity, sleep, and food logs on a daily basis. As a result, our proposed method identified 4 direct causal features from 103 variables and showed the highest prediction performance with the causal features. Based on the results, we discussed important features in predicting weight maintenance rate and behaviors that should be monitored to avoid weight maintenance failure.
