Predicting the mental health of rural Bangladeshi children in coronavirus disease 2019

Moon, Nazmun Nessa; Hossain, Refath Ara; Jahan, Israt; Shakil, Shahriar; Uddin, Shihab; Hassan, Mahmudul; Nur, Fernaz Narin

doi:10.11591/ijece.v12i5.pp5501-5510

Cited by 4 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has been determined that COVID-19 significantly impacts a number of variables that affect children's healthcare in ways that are not just medical but also social, psychological, economic, and educational. All of these factors may have affected the mental development of kids, particularly those who live in rural areas [30], [31], [32].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Bangladeshi Urban Children’s Mental Health for the Effect of Mobile Gaming Using Machine Learning

Hasan,

Bonny,

Das

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

The popularity of mobile gaming is increasing globally, fueled by technological advancements, high-quality smartphones with gaming features, and widespread internet access. A large number of young people and also children are mainly involved in different types of mobile gaming, primarily online mobile gaming, which is a matter of concern for society. This addiction leads to various psychological issues, including mental health problems, loneliness, introversion, insomnia, and a lack of self-control. Bangladesh’s lack of public mental health facilities exacerbates this challenge, particularly in rural areas. To address this issue, we have developed a machine learning model to predict the level of mental health issues faced by Bangladeshi urban children due to gaming addiction. A total of 1996 data were collected from urban parents about their children's gaming activities and categorized them as 'Serious,' 'Partial,' and 'Normal.' The dataset was then split into a 70:30 ratio for training and testing purposes. K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multinomial Naïve Bayes, & Random Forest were applied as the machine learning approaches for prediction. Here, the Support Vector Machine gives good accuracy (92.75%), and other classifiers have different accuracy. The research highlights the significance of addressing gaming addiction in children and the potential of machine learning technology in predicting mental health issues, particularly in the absence of public mental health services. The study concludes by proposing the expansion of the research to include smartphone and mobile gaming addiction among Bangladeshi rural children and ensure a holistic approach to tackling the broader issue of technology-related addiction.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of Bangladeshi Urban Children’s Mental Health for the Effect of Mobile Gaming Using Machine Learning

Hasan,

Bonny,

Das

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

show abstract

Determining The Parameter K of K-Nearest Neighbors (KNN) using Random Grid Search

Siregar,

Candra,

Sutarman

2024

2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA)

View full text Add to dashboard Cite

Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review

Alam,

Sajib,

Rahman

et al. 2024

J Med Internet Res

View full text Add to dashboard Cite

Background The rapid advancement of digital technologies, particularly in big data analytics (BDA), artificial intelligence (AI), machine learning (ML), and deep learning (DL), is reshaping the global health care system, including in Bangladesh. The increased adoption of these technologies in health care delivery within Bangladesh has sparked their integration into health care and public health research, resulting in a noticeable surge in related studies. However, a critical gap exists, as there is a lack of comprehensive evidence regarding the research landscape; regulatory challenges; use cases; and the application and adoption of BDA, AI, ML, and DL in the health care system of Bangladesh. This gap impedes the attainment of optimal results. As Bangladesh is a leading implementer of digital technologies, bridging this gap is urgent for the effective use of these advancing technologies. Objective This scoping review aims to collate (1) the existing research in Bangladesh’s health care system, using the aforementioned technologies and synthesizing their findings, and (2) the limitations faced by researchers in integrating the aforementioned technologies into health care research. Methods MEDLINE (via PubMed), IEEE Xplore, Scopus, and Embase databases were searched to identify published research articles between January 1, 2000, and September 10, 2023, meeting the following inclusion criteria: (1) any study using any of the BDA, AI, ML, and DL technologies and health care and public health datasets for predicting health issues and forecasting any kind of outbreak; (2) studies primarily focusing on health care and public health issues in Bangladesh; and (3) original research articles published in peer-reviewed journals and conference proceedings written in English. Results With the initial search, we identified 1653 studies. Following the inclusion and exclusion criteria and full-text review, 4.66% (77/1653) of the articles were finally included in this review. There was a substantial increase in studies over the last 5 years (2017-2023). Among the 77 studies, the majority (n=65, 84%) used ML models. A smaller proportion of studies incorporated AI (4/77, 5%), DL (7/77, 9%), and BDA (1/77, 1%) technologies. Among the reviewed articles, 52% (40/77) relied on primary data, while the remaining 48% (37/77) used secondary data. The primary research areas of focus were infectious diseases (15/77, 19%), noncommunicable diseases (23/77, 30%), child health (11/77, 14%), and mental health (9/77, 12%). Conclusions This scoping review highlights remarkable progress in leveraging BDA, AI, ML, and DL within Bangladesh’s health care system. The observed surge in studies over the last 5 years underscores the increasing significance of AI and related technologies in health care research. Notably, most (65/77, 84%) studies focused on ML models, unveiling opportunities for advancements in predictive modeling. This review encapsulates the current state of technological integration and propels us into a promising era for the future of digital Bangladesh.

show abstract

Predicting the mental health of rural Bangladeshi children in coronavirus disease 2019

Cited by 4 publications

References 23 publications

Prediction of Bangladeshi Urban Children’s Mental Health for the Effect of Mobile Gaming Using Machine Learning

Prediction of Bangladeshi Urban Children’s Mental Health for the Effect of Mobile Gaming Using Machine Learning

Determining The Parameter K of K-Nearest Neighbors (KNN) using Random Grid Search

Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review

Contact Info

Product

Resources

About