Feature Contributions and Predictive Accuracy in Modeling Adolescent Daytime Sleepiness Using Machine Learning: The MeLiSA Study.

Excessive daytime sleepiness (EDS) among adolescents poses significant risks to academic performance, mental health, and overall well-being. This study examines the prevalence and risk factors of EDS in adolescents in Bangladesh and utilizes machine learning approaches to predict the risk of EDS. A cross-sectional study was conducted among 1496 adolescents using a structured questionnaire.

Exploring Sleep Duration and Insomnia Among Prospective University Students: A Study with Geographical Data and Machine Learning Techniques.

Background: Sleep disruptions among prospective university students are increasingly recognized for their potential ramifications on academic achievement and psychological well-being. But, information regarding sleep issues among students preparing for university entrance exams is unknown. Thus, this study aimed to investigate the prevalence and factors associated with sleep duration and insomnia among university entrance test-takers in Bangladesh, utilizing both traditional statistical analyses and advanced geographic information system and machine learning techniques for enhanced predictive capability.

Analyzing the Impact of Demographic Variables on Spreading and Forecasting COVID-19.

The aim of this study is to analyse the coronavirus disease 2019 (COVID-19) outbreak in Bangladesh. This study investigates the impact of demographic variables on the spread of COVID-19 as well as tries to forecast the COVID-19 infected numbers. First of all, this study uses Fisher's Exact test to investigate the association between the infected groups of COVID-19 and demographical variables.