AI Medical Compendium Journal:
BMC public health

Showing 21 to 30 of 81 articles

Study on the prediction performance of AIDS monthly incidence in Xinjiang based on time series and deep learning models.

BMC public health
OBJECTIVE: AIDS is a highly fatal infectious disease of Class B, and Xinjiang is a high-incidence region for AIDS in China. The core of prevention and control lies in early monitoring and early warning. This study aims to identify the best model for ...

Predicting sleep quality among college students during COVID-19 lockdown using a LASSO-based neural network model.

BMC public health
BACKGROUND: In March 2022, a new outbreak of COVID-19 emerged in Quanzhou, leading to the implementation of strict lockdown management measures in colleges. While existing research has indicated that the pandemic has had a significant impact on sleep...

Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement longitudinal study (CHARLS).

BMC public health
BACKGROUND: Due to the ageing population and evolving lifestyles occurring in China, middle-aged and elderly populations have become high-risk groups for cardiovascular disease (CVD). The aim of this study was to analyse the incidence characteristics...

Predictors of depression among Chinese college students: a machine learning approach.

BMC public health
BACKGROUND: Depression is highly prevalent among college students, posing a significant public health challenge. Identifying key predictors of depression is essential for developing effective interventions. This study aimed to analyze potential depre...

Development of a machine learning model related to explore the association between heavy metal exposure and alveolar bone loss among US adults utilizing SHAP: a study based on NHANES 2015-2018.

BMC public health
BACKGROUND: Alveolar bone loss (ABL) is common in modern society. Heavy metal exposure is usually considered to be a risk factor for ABL. Some studies revealed a positive trend found between urinary heavy metals and periodontitis using multiple logis...

A novel graph neural network based approach for influenza-like illness nowcasting: exploring the interplay of temporal, geographical, and functional spatial features.

BMC public health
BACKGROUND: Accurate and timely monitoring of influenza prevalence is essential for effective healthcare interventions. This study proposes a graph neural network (GNN)-based method to address the issue of cross-regional connectivity in predicting in...

Explore the factors related to the death of offspring under age five and appraise the hazard of child mortality using machine learning techniques in Bangladesh.

BMC public health
BACKGROUND: Child mortality is a reliable and significant indicator of a nation's health. Although the child mortality rate in Bangladesh is declining over time, it still needs to drop even more in order to meet the Sustainable Development Goals (SDG...

Predicting bullying victimization among adolescents using the risk and protective factor framework: a large-scale machine learning approach.

BMC public health
BACKGROUND: Bullying, encompassing physical, psychological, social, or educational harm, affects approximately 1 in 20 United States teens aged 12-18. The prevalence and impact of bullying, including online bullying, necessitate a deeper understandin...

Predicting home delivery and identifying its determinants among women aged 15-49 years in sub-Saharan African countries using a Demographic and Health Surveys 2016-2023: a machine learning algorithm.

BMC public health
BACKGROUND: Birth-related mortality is significantly increased by home births without skilled medical assistance during delivery, presenting a major risk to the public's health. The objective of this study is to predict home delivery and identify the...

The association of lifestyle with cardiovascular and all-cause mortality based on machine learning: a prospective study from the NHANES.

BMC public health
BACKGROUND: Lifestyle and cardiovascular mortality and all-cause mortality have been exhaustively explored by traditional methods, but the advantages of machine learning (ML) over traditional methods may lead to different or more precise conclusions....