Machine Learning Insights into Mobile Phone Usage and Its Effects on Student Health and Academic Achievement
- Title
- Machine Learning Insights into Mobile Phone Usage and Its Effects on Student Health and Academic Achievement
- Creator
- Biswas P.; Krishnan D.R.; Basha M.S.A.; Sucharitha M.M.
- Description
- The research intends to find how students' health and academic performance are affected by their smartphone use. Considering how widely smartphones are used among students, it is important to know how they could affect health and learning results. This study aims to create prediction models that can spot trends and links between smartphone usage, health ratings, and academic achievement, thereby offering insightful information for teachers and legislators to encourage better and more efficient use among their charges. Data on students' mobile phone use, health evaluations, and academic achievement were gathered for the study. Preprocessing of the dataset helped to translate categorical variables into numerical forms and manage missing values. Trained and assessed were many machine learning models: Random Forest, SVM, Decision Tree, Gradient Boosting, Logistic Regression, AdaBoost, and K-Nearest Neighbors (KNN). The models' performance was evaluated in line with their accuracy in influencing performance effects and health ratings. Predictive accuracy was improved by use of feature engineering and model optimization methods. With 63.33% of accuracy for estimating health ratings, the SVM model was most successful in capturing the link between smartphone usage and health results. With an accuracy of 50%, logistic regression performed very well in forecasting performance effect, therefore stressing important linear connections between consumption habits and academic success. Random Forest and Decision Tree models were less successful for performance impact even if they showed strong performance in health forecasts. These results highlight the need of customized treatments to reduce the detrimental consequences of too high mobile phone use on students' academic performance and health. 2024 IEEE.
- Source
- 2024 3rd International Conference for Advancement in Technology, ICONAT 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Academic Achievement; Machine Learning Models; Smart Phone; Student Health
- Coverage
- Biswas P., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Krishnan D.R., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Basha M.S.A., GITAM School of Business, (Deemed to Be University), Bengaluru, India; Sucharitha M.M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835035417-1
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Biswas P.; Krishnan D.R.; Basha M.S.A.; Sucharitha M.M., “Machine Learning Insights into Mobile Phone Usage and Its Effects on Student Health and Academic Achievement,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19007.