UNSTRUCTURED Background: The objective of this study was to address the prevalent issue of sleep disturbance among college students, which can lead to a range of mental and physical disorders. The identification of potential predictors and the development of an accurate prediction model are essential steps for the early detection of and appropriate intervention in sleep disturbances. However, previous studies have encountered notable limitations. Objective: This study aimed to provide a fresh perspective by developing and validating a model for the prediction of sleep quality among college students, which will improve the accuracy of predictions and facilitate timely interventions. Mehods: We analyzed data from 20,645 college students between 5 April and 16 April 2022 in Fujian Province, China.First, the Pittsburgh Sleep Quality Index (PSQI) scale, a self-designed general data questionnaire, and a sleep quality influencing factor questionnaire were conducted among the participants. Second, the collected data were used to select appropriate variables by comparing the outcomes of a multinomial logistic regression, LASSO regression, and Boruta feature selection. The data were then divided into a training–testing set (70%) and an independent validation set (30%) using stratified sampling. We developed and validated six machine learning techniques, which included an artificial neural network, a decision tree, a gradient-boosting tree, a k-nearest neighbor, a naïve Bayes, and a random forest. Finally, an online sleep evaluation website was established based on the best-fitting prediction model. Results: The mean global PSQI score was 6.02±3.112, and the sleep disturbance rate was 28.9% (defined as a global PSQI score of > 7 points). The LASSO regression model was preferred because it contained only the following eight predictors: age, specialty, respiratory history, coffee consumption, staying up late, long hours online, sudden changes, and impatient closed-loop management. Among the generated models, the artificial neural network (ANN) model was proven to have the best performance, with a cutoff, AUROC, accuracy, sensitivity, specificity, precision, F1-score, and KAPPA of 0.710, 0.713 (95%CI 0.696-0.730), 0.669 (95%CI 0.669-0.669), 0.682 (95%CI 0.699-0.665), 0.637 (95%CI 0.665-0.610), 0.822 (95%CI 0.837-0.807), 0.745 (95%CI 0.729-0.795), and 0.284 (95%CI 0.313-0.255), respectively. In addition, it had a Brier score of 0.182. The calibration curves showed good agreement between the predictions and the observations. A decision curve analysis demonstrated that the model could achieve a net benefit. A clinical impact curve confirmed the high clinical efficiency of the prediction model. Conclusions: The prediction model, which incorporated eight predictors, was built using a LASSO regression and an ANN to estimate the probability of sleep disturbance among college students. This model may be utilized as an intuitive and practical tool for sleep quality predictions to support better management and healthcare on college campuses.
UNSTRUCTURED Background: Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of methods have been applied to predict the occurrence of OSA. Objective: This study aimed to develop and evaluate OSA prediction models using simple and accessible parameters, combined with multiple machine learning algorithms, and integrate them into a cloud-based mobile sleep medicine management platform for clinical use. Methods: The study data were obtained from the clinical data of 610 patients who underwent polysomnography (PSG) at the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University between January 2021 and December 2022. The participants were randomly divided into a training–test group (80%) and an independent validation group (20%). The logistic regression, artificial neural network, naïve Bayes, support vector machine, random forest, and decision tree algorithms were used with age, gender, BMI, and mean heart rate during sleep as predictors to build a risk prediction model for moderate-to-severe OSA. To evaluate the performance of the models, we calculated the area under the receiver operating curve (AUROC), accuracy, recall, specificity, precision, and F1-score for the independent validation set. In addition, the calibration curve, decision curve, and clinical impact curve were generated to determine clinical usefulness. Results: Age, gender, BMI, and mean heart rate during sleep were significantly associated with OSA. The ANN model had the best efficacy compared with the other prediction algorithms. The AUROC, accuracy, recall, specificity, precision, F1-score, and Brier score were 0.804, 0.699, 0.865, 0.615, 0.532, 0.659, and 0.165, respectively, for the ANN model. The AUROCs for the LR, NB, SVM, RF, and DT models were 0.802, 0.797, 0.792, 0.784, and 0.704, respectively. Conclusions: The six models based on four simple and easily accessible parameters effectively predicted moderate-to-severe OSA in patients with PSG screening, with the ANN model having the best performance. These models can provide a reliable tool for early OSA diagnosis, and their integration into a cloud-based mobile sleep medicine management platform could improve clinical decision making.
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