A Multiple Logistic Regression Model Based on Gamma‐Glutamyl Transferase as a Biomarker for Early Prediction of Drug‐Induced Liver Injury in Vietnamese Patients

Nguyen, Phuong Thi Thu; Hoang, Dung Van; Pham, Khue M.; Nguyen, Hoi Thanh

doi:10.1002/jcph.1955

Cited by 1 publication

(1 citation statement)

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“…In addition, due to the random forest model to build a decision tree and perform multiple feature selection and integration of the operation, its training time is relatively long ( 58 ). In contrast, the logistic regression model, as a generalized linear model, uses the least squares method to fit the model and thus has high accuracy ( 59 ). Logistic regression models can make predictions and explore the direction and degree of influence between independent and dependent variables, so they have better explanatory power ( 49 ).…”

Section: Discussionmentioning

confidence: 99%

A machine learning model to predict the risk of depression in US adults with obstructive sleep apnea hypopnea syndrome: a cross-sectional study

Li,

Ai,

Liang

2024

Front. Public Health

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ObjectiveDepression is very common and harmful in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). It is necessary to screen OSAHS patients for depression early. However, there are no validated tools to assess the likelihood of depression in patients with OSAHS. This study used data from the National Health and Nutrition Examination Survey (NHANES) database and machine learning (ML) methods to construct a risk prediction model for depression, aiming to predict the probability of depression in the OSAHS population. Relevant features were analyzed and a nomogram was drawn to visually predict and easily estimate the risk of depression according to the best performing model.Study designThis is a cross-sectional study.MethodsData from three cycles (2005–2006, 2007–2008, and 2015–2016) were selected from the NHANES database, and 16 influencing factors were screened and included. Three prediction models were established by the logistic regression algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, and random forest algorithm, respectively. The receiver operating characteristic (ROC) area under the curve (AUC), specificity, sensitivity, and decision curve analysis (DCA) were used to assess evaluate and compare the different ML models.ResultsThe logistic regression model had lower sensitivity than the lasso model, while the specificity and AUC area were higher than the random forest and lasso models. Moreover, when the threshold probability range was 0.19–0.25 and 0.45–0.82, the net benefit of the logistic regression model was the largest. The logistic regression model clarified the factors contributing to depression, including gender, general health condition, body mass index (BMI), smoking, OSAHS severity, age, education level, ratio of family income to poverty (PIR), and asthma.ConclusionThis study developed three machine learning (ML) models (logistic regression model, lasso model, and random forest model) using the NHANES database to predict depression and identify influencing factors among OSAHS patients. Among them, the logistic regression model was superior to the lasso and random forest models in overall prediction performance. By drawing the nomogram and applying it to the sleep testing center or sleep clinic, sleep technicians and medical staff can quickly and easily identify whether OSAHS patients have depression to carry out the necessary referral and psychological treatment.

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Section: Discussionmentioning

confidence: 99%