AIMC Topic: Bangladesh

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Prevalence, associated factors, and machine learning-based prediction of depression, anxiety, and stress among university students: a cross-sectional study from Bangladesh.

Journal of health, population, and nutrition
BACKGROUND: Mental health challenges are a growing global public health concern, with university students at elevated risk due to academic and social pressures. Although several studies have exmanined mental health among Bangladeshi students, few hav...

A comparative evaluation of multiple machine learning approaches for forecasting dengue outbreaks in Bangladesh.

Scientific reports
This study aims to forecast dengue incidence in Bangladesh by applying and comparing machine learning techniques. Dengue surveillance data from January 1, 2022, to December 1, 2023, for five divisions of Bangladesh was obtained from the Directorate G...

Identifying key influencers of patient satisfaction using an explainable machine learning approach.

Scientific reports
Patient satisfaction is a crucial measure of healthcare quality, influencing both health outcomes and care experiences. This study aims to identify the factors influencing patient satisfaction in healthcare facilities using machine learning algorithm...

Prioritizing geochemical drivers of groundwater quality and health risks in coastal aquifers of Bangladesh using machine learning algorithms.

Environmental geochemistry and health
This study aims to evaluate key parameters of groundwater quality and associated health risks in three coastal aquifers of Cox's Bazar, Bangladesh, with a focus on manganese contamination and geochemical processes. A total of 288 groundwater samples ...

Cardiovascular risk prediction and influencing predictors identification among Bangladeshi individuals using machine learning algorithms and association rule mining.

PloS one
BACKGROUND: Cardiovascular disease (CVD) encompasses a group of disorders that affect the heart and blood vessels, making it one of the leading causes of death globally, including in Bangladesh. Applying predictive modeling for the early identificati...

A hybrid framework of statistical, machine learning, and explainable AI methods for school dropout prediction.

PloS one
Student dropout is a significant challenge in Bangladesh, with serious impacts on both educational and socio-economic outcomes. This study investigates the factors influencing school dropout among students aged 6-24 years, employing data from the 201...

A robust hydroponic system for horticulture farming using deep learning, IoT, and mobile application.

PloS one
Due to limited literacy among root-level farmers, hydroponic farming in Bangladesh faces significant challenges. Therefore, there is a demand for easy-to-use technical systems to help farmers to monitor and operate smart systems. To address the issue...

Indigenous wood species classification using a multi-stage deep learning with grad-CAM explainability and an ensemble technique for Northern Bangladesh.

PloS one
Wood species recognition has recently emerged as a vital field in the realm of forestry and ecological conservation. Early studies in this domain have offered various methods for classifying distinct wood species found worldwide using data collected ...

An illustration of multi-class roc analysis for predicting internet addiction among university students.

PloS one
The internet is one of the essential tools today, and its impact is particularly felt among university students. Internet addiction (IA) has become a serious public health issue worldwide. This multi-class classification study aimed to identify the p...

Groundwater quality assessment and health risk evaluation for schoolchildren in Mujibnagar, Bangladesh: safe consumption guidelines using artificial neural network modeling.

Environmental geochemistry and health
Groundwater is a vital source of drinking water in Bangladesh, with tubewells commonly used, particularly in schools. This study assessed the quality of tubewell water in the southwest region, focusing on iron (Fe), arsenic (As), pH, electrical condu...