Use of machine learning to identify risk factors for insomnia

Abstract

Importance Sleep is critical to a person’s physical and mental health, but there are few studies systematically assessing risk factors for sleep disorders. Objective The objective of this study was to identify risk factors for a sleep disorder through machine-learning and assess this methodology. Design, setting, and participants A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. Methods A physician diagnosis of insomnia was the outcome of this study. Univariate logistic models, with insomnia as the outcome, were used to identify covariates that were associated with insomnia. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the cover statistic to identify risk factors for insomnia. Shapely Additive Explanations (SHAP) were utilized to visualize the relationship between these potential risk factors and insomnia. Results Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51% were female, 3,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of another race. The machine learning model had 64 out of a total of 684 features that were found to be significant on univariate analysis (P<0.0001 used). These were fitted into the XGBoost model and an AUROC = 0.87, Sensitivity = 0.77, Specificity = 0.77 were observed. The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were the Patient Health Questionnaire depression survey (PHQ-9) (Cover = 31.1%), age (Cover = 7.54%), physician recommendation of exercise (Cover = 3.86%), weight (Cover = 2.99%), and waist circumference (Cover = 2.70%). Conclusion Machine learning models can effectively predict risk for a sleep disorder using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.

Enhancing digital health services: A machine learning approach to personalized exercise goal setting

“…We are going to expand our data collection to involve a more extensive range of users from various backgrounds, therefore obtaining insights on exercise guidance tailored to specific groups, such as for pregnant women with diabetes. 37 On the other hand, recent studies have utilized machine learning methods and factor analysis to identify risk factors in various areas, such as sleep disorders, 10 musculoskeletal complications in diabetes, 38 blood lead levels’ impact, 39 and the influence of diet on depression. 40 Drawing on the methods used in these works, we could design machine learning approach to identify the intervention factors associated with exercise performance.…”

“… 8 Recent research has emphasized the potential of machine learning in strengthening health intervention services. 9 , 10 These studies emphasize the ongoing development and significance of integrating advanced data-driven methodologies into digital health services. Moreover, machine learning applications have made notable advancements in various health fields, such as epilepsy diagnostics, 11 and cancer prognosis.…”

Application of a transparent artificial intelligence algorithm for US adults in the obese category of weight

“…SHAP explanations provide transparent and interpretable insights into the machine learning models used to predict obesity and help identify the most influential features driving the predictions. This enhances our understanding of the complex relationships between various factors and the obese category of weight [5,6].…”

Increased vigorous exercise and decreased sedentary activities are associated with decreased depressive symptoms in United States adults: Analysis of The National Health and Nutrition Examination Survey (NHANES) 2017–2020

“…For the relevant covariates on demographics and exercise, chi‐squared was used for categorical variables for continuous variables, the Shapiro–Wilk test for normality was performed and t ‐tests were performed for continuous and normally distributed variables and non‐parametric Wilcoxon tests were utilized for non‐normally distributed variables to compare differences amongst those with clinical depression and those without. 22 Univariable models assessed the effect of vigorous exercise and sedentary activity on clinical depression risk. Next multivariable models were used to determine the effect of vigorous exercise and sedentary activity after controlling for confounding variables (age, race, education, sex, income, and work schedule).…”