# A Study on ML-Based Sleep Score Model Using Lifelog Data
## 4 Discussion
In the study of [PERSON] (2018) [42], the PDO was set at 58.43994, which is similar to this study. A study on the optimized PDO setting will be conducted in the future. Studies using stacking machine learning algorithms to improve performance were presented ([PERSON], 2020; [PERSON], 2018) [64; 65], and [PERSON] (2022) [66] added CV (cross-validation) to the stacking technique to prevent overfitting, which is similar to the method proposed in this study. As a result of comparing and reviewing the results and methodology of previous studies with this study, most of the results were consistent. Therefore, it is recommended to measure sleep quality and generate an objective score using lifelog data and machine learning algorithms. Since this method is based on the data of the user's life pattern, it is expected that the more data that are accumulated over time, the more accurate the quality of sleep can be predicted and the more accurate the sleep habit score can be generated. The limitations of this study are as follows. Since we created the sleep score by focusing on sleep habits and behaviors (sleep hygiene) rather than sleep quality itself, even if the definition of good sleep presented in previous studies is not met, expert review shows that good sleep quality can occur or vice versa. In the future, this research will go beyond the rating of sleep habit level to evaluate overall lifestyle, including walking habits and weight habits. We also plan to conduct simulations using an optimization algorithm that goes beyond simple ratings to perform additional analysis of optimal combinations and factors to increase sleep scores. This study used a linear model logistic model, but future research will study a new technique that calculates weights with a nonlinear model and scores them. In addition, good/bad sleep habits were classified with a simple linear model, and intuitive weights were obtained. Based on this, intermediate sleep habit data in which various factors were mixed were classified using a more complex model--a stacking machine learning model. However, as the efficiency can be increased as the number of steps is reduced, a study on a model that can be solved end to end in a single step will be conducted in future work. Lastly, instead of using all of the various indicators discovered in previous studies, we can consider and study regularization models such as LASSO that can identify features that are actually important and those that can be discarded. Through these studies, it is expected that our research will contribute to private medical insurance and comprehensive health management more substantially. Conceptualization, J.K. and M.P. ; data curation, J.K.; formal analysis, J.K.; funding acquisition, M.P. ; methodology, J.K. and M.P. ; supervision, M.P. ; validation, J.K.; visualization, J.K.; writing--original draft preparation, J.K. and M.P. ; writing--review and editing, M.P. All authors have read and agreed to the published version of the manuscript. This work was supported by a research grant from Seoul Women's University (2021-0423). Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.